{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Simulation with ``DSRunner``\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/brainpy/blob/master/docs_version2/tutorial_simulation/simulation_dsrunner.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/brainpy/blob/master/docs_version2/tutorial_simulation/simulation_dsrunner.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "@[Tianqiu Zhang](mailto:tianqiuakita@gmail.com) @[Chaoming Wang](mailto:adaduo@outlook.com) @[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The convenient simulation interface for dynamical systems in BrainPy is implemented by ``brainpy.dyn.DSRunner``. It can simulate various levels of models including channels, neurons, synapses and systems. In this tutorial, we will introduce how to use ``brainpy.dyn.DSRunner`` in detail."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:04:45.488258Z",
     "start_time": "2025-10-06T04:04:41.097013Z"
    }
   },
   "source": [
    "import brainpy as bp\n",
    "import brainpy.math as bm\n",
    "\n",
    "bm.set_platform('cpu')"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:04:45.503114Z",
     "start_time": "2025-10-06T04:04:45.496938Z"
    }
   },
   "source": [
    "bp.__version__"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.0.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Initializing a `DSRunner`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Generally, we can initialize a runner for dynamical systems with the format of:\n",
    "\n",
    "```python\n",
    "runner = DSRunner(\n",
    "      target=instance_of_dynamical_system,\n",
    "      inputs=inputs_for_target_DynamicalSystem,\n",
    "      monitors=interested_variables_to_monitor,\n",
    "      jit=enable_jit_or_not,\n",
    "      progress_bar=report_the_running_progress,\n",
    "      numpy_mon_after_run=transform_into_numpy_ndarray\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "In which\n",
    "- ``target`` specifies the model to be simulated. It must an instance of [brainpy.DynamicalSystem](../apis/auto/simulation/generated/brainpy.simulation.brainobjects.DynamicalSystem.rst).\n",
    "- ``inputs`` is used to define the input operations for specific variables.\n",
    "    - It should be the format of `[(target, value, [type, operation])]`, where `target` is the input target, `value` is the input value, `type` is the input type (such as \"fix\", \"iter\", \"func\"), `operation` is the operation for inputs (such as \"+\", \"-\", \"*\", \"/\", \"=\"). Also, if you want to specify multiple inputs, just give multiple ``(target, value, [type, operation])``, such as ``[(target1, value1), (target2, value2)]``.\n",
    "    - It can also be a function, which is used to manually specify the inputs for the target variables. This input function should receive one argument `tdi` which contains the shared arguments like time `t`, time step `dt`, and index `i`.\n",
    "- ``monitors`` is used to define target variables in the model. During the simulation, the history values of the monitored variables will be recorded. It can also to monitor variables by callable functions and it should be a `dict`. The `key` should be a string for later retrieval by `runner.mon[key]`. The `value` should be a callable function which receives an argument: `tdt`.\n",
    "- ``jit`` determines whether to use [JIT compilation](../tutorial_math/compilation.ipynb) during the simulation.\n",
    "- ``progress_bar`` determines whether to use progress bar to report the running progress or not.\n",
    "- ``numpy_mon_after_run`` determines whether to transform the JAX arrays into numpy ndarray or not when the network finishes running."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Running a `DSRunner`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "After initialization of the runner, users can call `.run()` function to run the simulation. The format of function `.run()` is showed as follows:\n",
    "```python\n",
    "runner.run(duration=simulation_time_length,\n",
    "           inputs=input_data,\n",
    "           reset_state=whether_reset_the_model_states,\n",
    "           shared_args=shared_arguments_across_different_layers,\n",
    "           progress_bar=report_the_running_progress,\n",
    "           eval_time=evaluate_the_running_time\n",
    "           )\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "In which\n",
    "- ``duration`` is the simulation time length.\n",
    "- ``inputs``  is the input data. If ``inputs_are_batching=True``, ``inputs`` must be a PyTree of data with two dimensions: `(num_sample, num_time, ...)`. Otherwise, the ``inputs`` should be a PyTree of data with one dimension: `(num_time, ...)`.\n",
    "- ``reset_state`` determines whether to reset the model states.\n",
    "- ``shared_args`` is shared arguments across different layers. All the layers can access the elements in ``shared_args``.\n",
    "- ``progress_bar`` determines whether to use progress bar to report the running progress or not.\n",
    "- ``eval_time`` determines whether to evaluate the running time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Here we define an E/I balance network as the simulation model."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:04:45.524174Z",
     "start_time": "2025-10-06T04:04:45.517704Z"
    }
   },
   "source": [
    "class EINet(bp.Network):\n",
    "    def __init__(self, scale=1.0, method='exp_auto'):\n",
    "        super(EINet, self).__init__()\n",
    "\n",
    "        # network size\n",
    "        num_exc = int(3200 * scale)\n",
    "        num_inh = int(800 * scale)\n",
    "\n",
    "        # neurons\n",
    "        pars = dict(V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.)\n",
    "        self.E = bp.neurons.LIF(num_exc, **pars, method=method)\n",
    "        self.I = bp.neurons.LIF(num_inh, **pars, method=method)\n",
    "\n",
    "        # synapses\n",
    "        prob = 0.1\n",
    "        we = 0.6 / scale / (prob / 0.02) ** 2  # excitatory synaptic weight (voltage)\n",
    "        wi = 6.7 / scale / (prob / 0.02) ** 2  # inhibitory synaptic weight\n",
    "        self.E2E = bp.synapses.Exponential(self.E, self.E, bp.conn.FixedProb(prob),\n",
    "                                           output=bp.synouts.COBA(E=0.), g_max=we,\n",
    "                                           tau=5., method=method)\n",
    "        self.E2I = bp.synapses.Exponential(self.E, self.I, bp.conn.FixedProb(prob),\n",
    "                                           output=bp.synouts.COBA(E=0.), g_max=we,\n",
    "                                           tau=5., method=method)\n",
    "        self.I2E = bp.synapses.Exponential(self.I, self.E, bp.conn.FixedProb(prob),\n",
    "                                           output=bp.synouts.COBA(E=-80.), g_max=wi,\n",
    "                                           tau=10., method=method)\n",
    "        self.I2I = bp.synapses.Exponential(self.I, self.I, bp.conn.FixedProb(prob),\n",
    "                                           output=bp.synouts.COBA(E=-80.), g_max=wi,\n",
    "                                           tau=10., method=method)"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Then we will wrap it into DSRunner for dynamic simulation. ``brainpy.DSRunner`` aims to provide model simulation with an outstanding performance. It takes advantage of the [structural loop primitive](../tutorial_math/control_flows.ipynb) to lower the model onto the XLA devices."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:04:53.156037Z",
     "start_time": "2025-10-06T04:04:45.535182Z"
    }
   },
   "source": [
    "# instantiate EINet\n",
    "net = EINet()\n",
    "# initialize DSRunner\n",
    "runner = bp.DSRunner(target=net,\n",
    "                     monitors=['E.spike'],\n",
    "                     inputs=[('E.input', 20.), ('I.input', 20.)],\n",
    "                     jit=True)\n",
    "# run the simulation\n",
    "runner.run(duration=1000.)\n",
    "bp.visualize.raster_plot(runner.mon.ts, runner.mon['E.spike'])"
   ],
   "outputs": [
    {
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     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "We have run a simple example of using `DSRunner`, but there are many advanced usages despite this. Next we will formally introduce two main aspects that will be used frequently in `DSRunner`: monitors and inputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Monitors in DSRunner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "In BrainPy, any instance of ``brainpy.DSRunner`` has a built-in monitor. Users can set up a monitor when initializing a runner. There are multiple methods to initialize a monitor. The first method is to initialize a monitor is through a list of strings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Initialization with a list of strings"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:04:55.687184Z",
     "start_time": "2025-10-06T04:04:55.682973Z"
    }
   },
   "source": [
    "# initialize monitor through a list of strings\n",
    "runner1 = bp.DSRunner(target=net,\n",
    "                      monitors=['E.spike', 'E.V', 'I.spike', 'I.V'],  # 4 elements in monitors\n",
    "                      inputs=[('E.input', 20.), ('I.input', 20.)],\n",
    "                      jit=True)"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "where all the strings corresponds to the name of the variables in the EI network:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:04:55.731489Z",
     "start_time": "2025-10-06T04:04:55.725829Z"
    }
   },
   "source": [
    "net.E.V, net.E.spike"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Variable(\n",
       "   value=ShapedArray(float32[3200]),\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " Variable(\n",
       "   value=ShapedArray(bool[3200]),\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Once we call the runner with a given time duration, the monitor will automatically record the variable evolutions in the corresponding models. Afterwards, users can access these variable trajectories by using .mon.[variable_name]. The default history times .mon.ts will also be generated after the model finishes its running. Let’s see an example.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:04:57.154298Z",
     "start_time": "2025-10-06T04:04:55.757774Z"
    }
   },
   "source": [
    "runner1.run(100.)\n",
    "bp.visualize.raster_plot(runner1.mon.ts, runner1.mon['E.spike'], show=True)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "fbb0260033d04c7fa0e8802ad38c7893"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\adadu\\\\miniconda3\\\\envs\\\\bdp\\\\Lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Initialization with index specification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The second method is similar to the first one, with the difference that the index specification is added. Index specification means users only monitor the specific neurons and ignore all the other neurons. Sometimes we do not care about all the contents in a variable. We may be only interested in the values at the certain indices. Moreover, for a huge network with a long-time simulation, monitors will consume a large part of RAM. Therefore, monitoring variables only at the selected indices will be more applicable. BrainPy supports monitoring a part of elements in a Variable with the format of tuple like this:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:04:58.416742Z",
     "start_time": "2025-10-06T04:04:57.192379Z"
    }
   },
   "source": [
    "# initialize monitor through a list of strings with index specification\n",
    "runner2 = bp.DSRunner(target=net,\n",
    "                      monitors=[('E.spike', [1, 2, 3]),  # monitor values of Variable at index of [1, 2, 3]\n",
    "                                'E.V'],  # monitor all values of Variable 'V'\n",
    "                      inputs=[('E.input', 20.), ('I.input', 20.)],\n",
    "                      jit=True)\n",
    "runner2.run(100.)\n",
    "print('The monitor shape of \"E.V\" is (run length, variable size) = {}'.format(runner2.mon['E.V'].shape))\n",
    "print('The monitor shape of \"E.spike\" is (run length, index size) = {}'.format(runner2.mon['E.spike'].shape))"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2cd7de8986db4ce78ee46d785c044d13"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The monitor shape of \"E.V\" is (run length, variable size) = (1000, 3200)\n",
      "The monitor shape of \"E.spike\" is (run length, index size) = (1000, 3)\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Explicit monitor target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The third method is to use a dict with the explicit monitor target. Users can access model instance and get certain variables as monitor target:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:05:00.105250Z",
     "start_time": "2025-10-06T04:04:58.454617Z"
    }
   },
   "source": [
    "# initialize monitor through a dict with the explicit monitor target\n",
    "runner3 = bp.DSRunner(target=net,\n",
    "                      monitors={'spike': net.E.spike, 'V': net.E.V},\n",
    "                      inputs=[('E.input', 20.), ('I.input', 20.)],\n",
    "                      jit=True)\n",
    "runner3.run(100.)\n",
    "print('The monitor shape of \"V\" is = {}'.format(runner3.mon['V'].shape))\n",
    "print('The monitor shape of \"spike\" is = {}'.format(runner3.mon['spike'].shape))"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "003d080350ea451583f1e15c124efdc0"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The monitor shape of \"V\" is = (1000, 3200)\n",
      "The monitor shape of \"spike\" is = (1000, 3200)\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Explicit monitor target with index specification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The fourth method is similar to the third one, with the difference that the index specification is added:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:05:01.454788Z",
     "start_time": "2025-10-06T04:05:00.139873Z"
    }
   },
   "source": [
    "# initialize monitor through a dict with the explicit monitor target\n",
    "runner4 = bp.DSRunner(target=net,\n",
    "                      monitors={'E.spike': (net.E.spike, [1, 2]),  # monitor values of Variable at index of [1, 2]\n",
    "                                'E.V': net.E.V},  # monitor all values of Variable 'V'\n",
    "                      inputs=[('E.input', 20.), ('I.input', 20.)],\n",
    "                      jit=True)\n",
    "runner4.run(100.)\n",
    "print('The monitor shape of \"E.V\" is = {}'.format(runner4.mon['E.V'].shape))\n",
    "print('The monitor shape of \"E.spike\" is = {}'.format(runner4.mon['E.spike'].shape))"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "904258749dc847da9322611622f9b81f"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The monitor shape of \"E.V\" is = (1000, 3200)\n",
      "The monitor shape of \"E.spike\" is = (1000, 2)\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "In spite of the four methods mentioned above, BrainPy also provides users a convenient parameter to monitor more complicate variables: `fun_monitor`. Users can use a function to describe monitor and pass to `fun_monitor`. `fun_monitor` must be a dict and the `key` should be a string for the later retrieval by `runner.mon[key]`, the `value` should be a callable function which receives an arguments: `tdi`. The format of `fun_monitor` is shown as below:\n",
    "```python\n",
    "fun_monitor = {'key_name': lambda tdi: body_func(tdi)}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Here we monitor a variable that"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:05:02.801303Z",
     "start_time": "2025-10-06T04:05:01.520532Z"
    }
   },
   "source": [
    "runner5 = bp.DSRunner(target=net,\n",
    "                      monitors={'E-I.spike': lambda: bm.concatenate((net.E.spike, net.I.spike), axis=0)},\n",
    "                      inputs=[('E.input', 20.), ('I.input', 20.)],\n",
    "                      jit=True)\n",
    "runner5.run(100.)\n",
    "bp.visualize.raster_plot(runner5.mon.ts, runner5.mon['E-I.spike'])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "627cf36277044a5f92a50820ad43b2f0"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\adadu\\\\miniconda3\\\\envs\\\\bdp\\\\Lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Inputs in DSRunner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "In brain dynamics simulation, various inputs are usually given to different units of the dynamical system. In BrainPy, `inputs` can be specified to runners for dynamical systems. The aim of ``inputs`` is to mimic the input operations in experiments like Transcranial Magnetic Stimulation (TMS) and patch clamp recording.\n",
    "\n",
    "``inputs`` should have the format like ``(target, value, [type, operation])``, where\n",
    "- ``target`` is the target variable to inject the input.\n",
    "- ``value`` is the input value. It can be a scalar, a tensor, or a iterable object/function.\n",
    "- ``type`` is the type of the input value. It support two types of input: ``fix`` and ``iter``. The first one means that the data is static; the second one denotes the data can be iterable, no matter whether the input value is a tensor or a function. The `iter` type must be explicitly stated.\n",
    "- ``operation`` is the input operation on the target variable. It should be set as one of `{ + , - , * , / , = }`, and if users do not provide this item explicitly, it will be set to '+' by default, which means that the target variable will be updated as ``val = val + input``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Users can also give multiple inputs for different target variables, like:\n",
    "\n",
    "```python\n",
    "\n",
    "inputs=[(target1, value1, [type1, op1]),\n",
    "        (target2, value2, [type2, op2]),\n",
    "              ... ]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Static inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The first example is providing static inputs. The excitation and inhibition neurons all receive the same current intensity:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:05:04.072291Z",
     "start_time": "2025-10-06T04:05:02.836219Z"
    }
   },
   "source": [
    "runner6 = bp.DSRunner(target=net,\n",
    "                      monitors=['E.spike'],\n",
    "                      inputs=[('E.input', 20.), ('I.input', 20.)],  # static inputs\n",
    "                      jit=True)\n",
    "runner6.run(100.)\n",
    "bp.visualize.raster_plot(runner6.mon.ts, runner6.mon['E.spike'])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "647e6999c4914181bcb9de47ef25583a"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\adadu\\\\miniconda3\\\\envs\\\\bdp\\\\Lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Iterable inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The second example is providing iterable inputs. Users need to set `type=iter` and pass an iterable object or function into `value`:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:05:06.185703Z",
     "start_time": "2025-10-06T04:05:04.101415Z"
    }
   },
   "source": [
    "I, length = bp.inputs.section_input(values=[0, 20., 0],\n",
    "                                    durations=[100, 1000, 100],\n",
    "                                    return_length=True,\n",
    "                                    dt=0.1)\n",
    "\n",
    "runner7 = bp.DSRunner(target=net,\n",
    "                      monitors=['E.spike'],\n",
    "                      inputs=[('E.input', I, 'iter'), ('I.input', I, 'iter')],  # iterable inputs\n",
    "                      jit=True)\n",
    "runner7.run(length)\n",
    "bp.visualize.raster_plot(runner7.mon.ts, runner7.mon['E.spike'])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/12000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "41386f761da649cf81234243005ab28e"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\adadu\\\\miniconda3\\\\envs\\\\bdp\\\\Lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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jjoPl5WU4ePDg8Pv3vvc9eN7zngcnnngiHHPMMfDMZz4TbrnllpEyvvSlL8G5554Lu3fvhr1798JLXvISuPvuu0fSfPCDH4QzzzwTpqam4MCBA3DJJZek+BwXINICGSMKMqNw8Vvk0ix+SsQSzJFjwKicpeC/UkWk5eOLrEmgYlGTwKspb0OpQqzrvcEyItsN2vyOCNid6iGKHPHOjGnEW1LKK03fhoeolCdK2ePlWn11QTEtuzQ8wOtrLb/m7bE8RJ4xWdLGDP9115xFOwYQ/dVf/RW8733vg8997nNw3XXXwa/92q/B0UcfDddccw0AAFx44YVw6qmnwmWXXQaHDh2C5eVleOxjHzvMf88998Di4iKcffbZcOWVV8LBgwdhfn4eXvnKVw7T3HDDDTA7OwsvetGL4Nprr4U3vvGNMDk5CZdeemmYz+0UVN22gtzKspr2EJUK5zYWZ1tj3BRfpVsnpelL5sA4+6UOZfgsUeBNUlt92qR8iab1gHemTD4u23nucd44mGsDlHIjhnqaPH7GTTsGEEl0wgknwB//8R/DrbfeCkcffTS8853vHH77zGc+A1VVwRVXXAEAAAcPHoSJiYkRr9Gb3/xm2LNnD9x1110AAPCyl70MHvGIR4zU8axnPSvlmhtXUHWJAm/TAsssirbKaANIZJRutI11A0vbBG9S2ZzfrNCqqyA8oV2n30t4qwNMKB8bGxvureOUIsfcsY4MgIz2Qak30qsr4xHV0kd45r+XxsrxMr2/txN5aynjzeWkGUy0THqvFKb3ANk4AeaOBET33HMPvP3tb4epqSn49Kc/DZdddhlUVQXf+ta3RtKddtpp8IY3vAEAAF796lfDGWecMfL9hhtugKqq4BOf+AQAADzhCU+AF77whSNp3vrWt8KePXtUXu6880647bbbhv9uuummsQCikkWYWag4CSNxHwCx+AZPAGmL0eNF+u4tIku4c4tGSxfZZpPKi14jQH/TeNLqzQAZq73Szchc8Hludzr2JYH3moBFfiIue63fS9YEPs5bcuyX8oE3HldVlQJEWvwgT6fFh/D1FekDPp+yIIbzoOXja1lSzNq60PiW5mr0SQ+pbuneKcmIsC7PjD5aWwIGMkacVl9JbBaOLY9jssq0DMSsDmqCdhQg+uQnPwnHHHMMTE5OwtzcHLzvfe8DAIC3ve1tMDU1tSn9ox/9aHjZy14GAABra2twzjnnjHz/zne+A1VVDWORTj/9dHjd6143kuZ973sfVFUF3/3ud0WeLrrooqFQo//aBESRRanliy6urKUQiW/wXNTaAvB4kb57Qj4KMKx0kUBsqTxJAEj80t+iAI7m1YCMxx/9r/VekiYAeVvo2GvCMuMJ8ICpRFq/l6yJ5eXlxjxEGW+hB0SRvBNEfH2VABlLIXrAyqqLr2VPGUYBrTZXrfq1+3wifYfrj95WTtMuLCxAVVUwMzNjyrNoPB39TcpjgSxJfkbHSQI32s3iEfmj9UMWFJbSjgJEd911F1x//fVw6NAheMUrXgHz8/Pw6U9/eksB0VZ4iKJCoA5lLZMmAVlE4XvlZviP1pflK8NL1HLLjEdpgGaUb66grX7R0lpzucn+zaTL5m9q7mf504BsyXrxqA3+I2U01RZt/mnpVlf1+3y8+UuBAb2tnKZdWVkZGs8a4F9dlZ/uQNIMD+0QyuTkJPR6PZiZmRm+lUb7xAKf0XHBevDpEQk8ckNAm7t15XoJ7ShAxOmss86CX/zFX9zSLTNObccQ8acsmlIG1kSsC2SylnCEp5K2RoViho9sG734k8xTAKXBnJHxzaSR+lPqMymd1N5MH1ht5vVGt2Gi/YbU1JUNnqHD01NZ0NS2Qltgqc38TZeFgCZyEtEaayxDU/yevECAE/UQUTBC+aPgDEHY7OzsJtBn8SSBL2kNra4+8GSNtjVLt+G5V8pqL334ti1nwI4GRD/5kz8JF1xwwTCo+i//8i+H3z772c+KQdVf+9rXhmne8pa3wJ49e+DOO+8EgPuDqhcXF0fqOO+887ZVUDXGHeAkjp6U8IQt/c7Tenm1OqQFkJnIFk8lbaX8ZJRHpG+ibaTjF/2utYenrTtONF8mjdSfUp956Ur6wGozr9fahon0nZYmEksWAUmeMpfGRFIsdSizzurkQcrIsKZ4yfaz9c0yFCIg1QNGGUNAMkJwbuIcWVxchJmZGVhfXx/2PfUoaXPXC4amfaPNRW48Sh40CdBhWSgX0GPWFIimtGMA0Ste8Qq4/PLL4Ytf/CJ88pOfhFe84hWwa9cu+Nu//VsAuP/Y/WmnnQYf+MAH4NChQ7CysgIrKyvD/Hjs/pxzzoGrrroKLr30Uti7d6947P6lL30pfOYzn4E/+IM/2HbH7qn1LE0gjToPUech0srbSg+RVG5dDxGvL7OlZ/VdlIfScfBIqqfutihPEymPp6uzNvv90YdnLR5KxqgEiFrjHDmooM13jZdorB8lD3zQ71owPHoY5+fnzXcoJYMmur45WfrLGyuet42wkR0DiJ773OfC/v37YWpqCvbu3QtnnXXWEAwBPHAx4wknnACzs7PwjGc8A26++eaRMm688UZ48pOfDLt374b5+Xl48YtfLF7M+MhHPhKmpqbgoQ996La7mJFSVqBuZdlt8rod6y2lw5XfcbQL6xjnqZTtRFYfUwWipYt6rCQlDuAD2cFgszeI/6aVLfEXIQuwWKDGSucFH2t1asHONMg+On/pXJdAReQUHW6hTU1NmYcNPIOGtstb53yHo8QQs36vSzsGEO0UGjcgKvG8RCgqHKMknXwptZAzHp62LIm2yLP8ALZXzEa0f8cxDpm+i767VOo1wm9Rj0sUVFrlWYqdbnloCtcCClxBSnx4W53cIyDVOxjor6GXzNvSMdTAW79/f0B09sbw5eVlqKoKer2equyxr7RTjBpI4YAyGhiNPEnXPmTWNfXwefmQ3/X19RAoHzd1gKhhGicgsqypuhS1oqIkAaJsmZLrNtuO7U4RL0edsdAs1br8ZjxEXp7SMYvkk+aQdkw9oly8cbLWZ9ajFS1PUzI8liozZpE5F/EQReptQinWXfcaeAMoe7trZmYGqqqCffv2qeMu/a6NAeWBgqMIYKany/Ch1eXl5U08R8H8wsLCJl5Kx9iaL+OQ5R0gapjGCYjoKZPSN2CiyslbbF5sjGaZ8zIjFrd14Vmm7Zn04wZWXj9klQzm40GUbfMs/eYBDQ2w1fHk8LT0VXDtlJi17YA8et4oac1QxWfd88SJnzCN9gFvT0lAavR27oyM0dJm17i0Hrwj69Eyo3FvUn4av4O84LyT5o5UJwVm9Hc8tk/jZL3Tjnze7d+/3/R2RYFpFCBG5JYEAOlhiNLbxaPUAaKGadweoswpLCmNly9ah3d6SqvP+zvShgiVeqOifJVQE54QzpdVpiZcm+IzOr8s67PEMqd1ZHnlQacSz7xMi0eNNEGfAX/WHOTKw+IrO5ctkBopW6vPSpvxAksyanV1FWZnZ4tvFPd4R9LmW7//gDePboFZa1BbK9LzLtJaiJx2xDIjgF4DwVljkQKxjI7CfMvLyyP92JR3W6IOEDVM4/YQ4YSNWi1Za8dC9fTviAUZ8QhlPU2Z150z8VZNWNUeWQLLqssCAJYAz5zSsECXRpHx5W2IWMlIGiAq4RX7YmVlJR10m51LnPeIpSy1I/LcQyQ+KOJN4kCszmlWyxPE5woPMC71EEVlUqZsiax5TME2P9kl3R2lyXOcO/QNu0g/W2ki/WsBWWk+aP3N+8iqW+JZWzttUAeIGqat8hBxasL74JHnKrUUS0QRWLxg3d67TpSXEusiAway/Z1RhtG6rG/RvqVpJQ+KRZqVqwlmTYFnAUGk/Vr7SgyJbByJlT4KIiTFGCkL2xvZnpP6pQ0lJNWT7dM6VKdNEgDT4oFoGpy/9F4gurbonJfWX2TLKGNMWfkkcEr/K8lSbZeAgzDPUxQBdm1RB4gapu1y7D6qyOvk8wSYZFl7lnzU2o8oB86Ld+eFRBkF29T+dsSK8/jSBHJE8dLfLUEf4dkDol55kXIylnFEyEa8lDSQVEtj1ZddCwB5I4CXRZWxBoolz1kbConOL1w3dGukBIxJc16bAxFvY8Zo4/NeKh9lRK/XG56i0wwPqX8kIEKBiyczefu0NvG/8f/pNqY1d7R1g+V4bwFG10Mb1AGihmmcgMii0slTomiz1gpfxCW8ZPiQyioFjFabmt7ftnj0+OcCTQNrknC3gIRWr/V7SdyS1J5IGzReqOLxePAs3YilbhkD/LumVLUyInfMaGPpKU/J0Iiuleh65b/TeJbV1VX1/S+PJCUuzQEcQy8eLdL/Gn9S+dj/tH2a4YHEn6uQxtADuRJlADvWKQW6e3OStz3i6YpuvbUBjDpA1DBtF0DUFpUCiabLqFteKWC06mragilRMNJ3C6xFPBWRer3fNTd7pC3RvrDivTzFQ0kTyBmvpAckpfZE0li/e4DA45G2kSvyyLwuBfDZ24sj7fHAfaRvI8ZAyRhFHo210nMwnY1n09pSkh5/X1hYGLlWwMo/GMiB4labAXJruJQ6QNQwHe6AKGoRSHk0a6QpntqwGLayriapLuioS5qQw29NCLg6gC5CdWJcmux/rZ0Rhd8mn1b9ltzgyrLpOdikYVGXt2wZHrArKTOSJ1oH/o7XAFh3ZFHg5sXUlWyzN0EdIGqY2gZE20EhexOWU1OuznEKpMwJle0wJiV8lFrfJWV4yrLEyuVlR96TqzNW41bWGjVxeqoOLxEA43kUaN7SO9SiaUs9V/S7JruamhN11lcbgDJbZmQNZ8psa4571AGihqltQNSUNV2Xh0wAseTqLFm42Xp5/fjfSP9pcSQaX02PSWn/NOEGl3iIKLgo31TJSEeTs0R5iCq4ttdPXW9P03kiPEXLjcwLTUGWrEWPNw8AWcAr6imJnIasA0ai68vi0evTUlDZVBszebdKz3WAqGFqExChkCm5qVkqq2RiS4KuxEtQMuElz1RbFlQbHqK2BULG4o7wE7HmM9Y4rxfd5ug6L7WQLe9TnbSlJM3TJniX8jTJU2m5Wp4M4PZ+579ZssTyWpWeBI3wWkeJZz1EJR4lvp6lU21SvigAbnIttbU2PeoAUcPUJiDCyUiPP9YtK1uGlK+O8q4rmLIW1FZSpp/asDazJPFQquS0tFE3e1MnS5pSYBaVKt42reI6YMCiqCL10nJevX6IGjkccJacBI20w5vLHvDN9CNvf0k7VldXR2J+pPnh8UmNJO1NtEhbvPZHy2iCOkDUMLXtIer373+HSXvTKFtW1jqLLuaMpV5KmhAq4acN/nh5+Iq39tJznbo1QdV0PaV7+6X9ad0oXUJRRZolb41I6aLfmlorUf4yV2JEtzozYCTS3lJwVVI2L6PEKKTfs/mlb3UNyqWlJaiqCnq9ngoWo23C8IKqqmB+ft6sXyuTB1lbMqxNw6EDRA3TOE6Z9fubX71uSrg35QHKCIgmedV+z9bX9KLDMauqCmZnZ03FEPWKUKEhKZvsGEQuJMxsu3BvTMRDoSmjNjxEJem0362+5pZ59qRadi56bbQuJYxc3kh5is7RyN1N0fZkjK0SkOmBqIjBZf2dNdiaDDDGdTg1NbXJo1PazxEPEc79xcXF4aWUlPiVFgi0pd2QKGgvoQ4QNUzjAETShGhKgdf1rrTpnYguWKl/sm3QFlypQsUy9+3bBxMTE+YTCtF3tqiFJr3sHRHStJ1o6S0sLKhK0HtBnFvC1i27VptKgb7n/YiWlQHbALbSonk0SzgCEqOxYZ4ssC4l5M97aGVlPTR1AC3noQljywLoTRiXTcrjJi98pescPUR140EB/Hf2EGhjvdYdQ3QOanO+aYMVqQNEDdNW3UPUlIeoLrU1UbNl1+HDyhst11Mk1laDpURouZ6HKMIT/c3bnirxENH0Ee9K3XkcHbuMlyHyu1Wv5R1oY0432ba649FEmaWeCw9kNgk0Sniw0nKAQC+uLAH7VFbgwRy8HJF6A6P3/1BQ7eVD8Nnr9WBubg6mp6eHN29LdUTGpS191wGihulwv5jRozaBWUQwetZ0RHBYJ/myFpTHh3cUOKoMIhZb9LemvCzR9E0q4vX1dZidnYX19XWzniiAiVIpv3UVp0ZNbrM0vabbNJoy1ES7Sj0q9HdruxuNImr00N88g4gbYLgFhZ5huoVFvWZe7A4FRP3+/eEAExMTsLS0pMomDOS2brTG9E2dps5SB4gapsMNELUFcJpQOpbHQ3PRS3kkRdnESb5ofVZsRUR5SH0p3aMkbZWUxLRkvScl5I2jRtH7oyxevbqlvCUAJJpHAs9eX9N+qDte2hyObktLoN7KW2ceNQ1mOa8RGSSVJz37gmBoYmJC3UKi9eB2NXpwUUZZz2ZIwEoDSOg1oo/Oau3i/z87OzsEOxoo29jYMJ/s4HzjFlvTJyMt6gBRwzSuGCLL+9BkHU28GyMJiBIrkeaRhKrFt9VnvFzqWm7jvqeMuzmiZKS+lJStBog8i42TNnZNWv6l868JzwivmwMjqZ0aELOUbRS8SSDd62v+Plid8dLWLz/YkamH/0b/LplHdeSVZRRwxRyRKxL/WAc+7IplRY664+8oJ/C1eDyturKyosalWeVRLwy9E4y2IyoTNjY21JPPknz1gC/y1+v1ip/LKaEOEDVM4zpl1qQXw6qjidM9USvKIw8EaGk1HjxemlTwlEoCazlpQsYiKZ0HyKLllLajtK5xkAeuIx4iaw5Z23sSH5KHqM6zF3XnTdTLo+Wnv9G/eV96fNI5XCKvLECEin5+ft4s31uPdLwsXrX5Qvvbm49WGVo6zcBsgrTxs047Up46QLSDaVweouXlZej1euZdRBFBYiF0LrCyF49Jr49HBHlkcZZYMNYpCMuKsk47lPAU8WJkxy6jIHk5mTZG+Y+0QUujtaWkjZ4yrstjxNLV8mrejMzc7vfbuXCR11HXmxvpH61fPAVf90i/Nf+pYSgBQC1v6ZzQtkUz/anJTt6PdB2XroNSGgz0F+9LjL2mqANEDdO4Hnfdt2+feUQaoLm7gNAC4y5yrSz8f8mDZX2L1OVZ1doC6vdHj4xrwkbiBYWt11dRa8YSsjxNVFmiYvTuONJ4oeNG80r14VbP7OysCCKtfuVgiqcZDAbDmIiZmZmRE2rWWGhgSVICs7Ozw1MuUv2SsrKuucgCBjr+EZ49BRjx8FlbGh55itP7zfIEUN5xDvMrKTTwyfuxRJnTMqR+tOrj20xSuSXrUPotCgysNazNM63tkbJLyQLy4wZBlDpA1DCN63FX3Ee2bvD1JlbU0tcElVYXlivdyIwCUjtBYNU1GAxgcnJyqIyt/pGAxPT09BBE8jgeC2Ropy54m6L73VjfysrK8Pp8LtQjCp4rChqgGbVYoxYyVc7Ly8swMzMjHp31+ozHzXCw0e8/ELPBg4JpuRpA4WCJ9yMaEgjmeL9oigl54tucJbFmvE5vC1VT/NockQj7nQa9Rj2VHLTw/vWUKc5zBGN07lKlyMGqRBGQILXLkgurqw/cD2Z5mWha3PLSxj4KHL30XuA6/9truwQu0cNmyXet7qx3WWuX10eldWWpA0QN0zg8RNaphwy69jwaksD1hLAlIOkC1AS8pVz6/f4QQKytrZk8S3zhbaorKyvDttNTH5wisQzYXu9EhCSIlpeXhwHNdJtAskolb4GlSDPA2bK0JeVsXZzmKbX19XWYnp4eOZ7LBTZuBy8tLanzXOsX7yI37A/rNm6pDxYWFkz3fp1Yu8g6pIoY25yx2PE24fn5+WHbI0Hd3nivro4G5UvyiPYd7S++1nlZWl9EwR/1YFr5pHq9tY5eak3WSL/jesfnMiLEx5jPFf63NCc8eaLFJmntoXIcPWTUe2u1zQPQ2pymRsm+fftai53tAFHDNM6g6syEksgTxJq1bG15catPKo+6nCOxAkgRa1QjzhcHZ5G+lCytqHVF80pKVLOcLWvIGj8sJ7K1GukLmtbyGGj9TIGM9+wMnWNNudWz6aP58Xd+0serkwNkzxMg9WvUM4VpuQczci+VBMYsUM7nNgX9dD7gW1r41AMHTnXG1wLlUrnUg4WkyT66LW3JQOl3XIvS0XSrXXScJUBkAVKvPEmmSSBQAuJYzvLy8vDIfmTbzQJOkuGI/4/zwzJi61IHiBqmcR679yZUnXK0754lnuHN+7uE52y+Un6zngFtoWtpI8Av0heZY+jRvrXaYglc+t26dI3OMasf6oKcJsuSwGnGcPH+1kBjRLFS69p7kZx6jTjop79ZgcYIhHbt2rUJhOF39Nbu27dv2H9UsWfXl9dfWjqsmwMx68JU6bAIJa1uGseV2faxQEtUVmjlefLBAuL8TqPIWHled0vGNnUVikUdIGqYDreLGQ83akqJZiz0krpLwGK0zuhvEkUAVjQ+K3JtgqU4rDIi/Ud/87ylUh7KozQXtO/S3OG/af3MAWVEKWOeubk590FPqrSpopN4s7ZhpS3pweCBk0UHDhwYORzB54y3jWONi3eilCt0Oj5efXWNNgoYve1BHAu6dayl4+215rt3Z5Jl8ND0FjDWKGMIUEBeeoAhSx0gapi2AhDVVfJNWtpauU0q9JJvKAg1138kP0/LrSePolYs5ZdenJYRBJrS0izKCIgB2OxBkAShBi6i8yFq9Q4Gg5HbdSlFPC5Yx+rqqhgjpFnjGHyLY6JtnVBe0DtD42j4PNAEP7ea6ZajptBo2yTQJ9XJ+47m1RRhZm3SfuBxIJJnwFujWj1ebBSWT8ct0iaa15qXVhr8hh4yLe5wMLBvf/bqk2STlM4Kwpb6QgOeJQYF/RtB2vr6ugnu2tJVSB0gapjGdeyeC5o6qLkt1C0J0WwdEeES+YaKhG8HlG5pAOTd1d6Rc14/8iudQvMIlQKPHdJAHBVoFnDjNyBLx441oaW1V7JCo0CTKz/JCvbq4FtCEr8UDGEMCXXle0qb3rmiWd1WTBnlg3ubLKBOAY90e7Wn9KTfMtuwfEyod0bzKmQUYFTJa/xkFW0EoFntonE3/GoJSriFeNRRR40EwnvtoN+4bJJ4k9ZaxAOIefE7xmHNz88X9SHKHzRYFxYWurfMDgdqGxBF97s51fWGZJC5J3AyZVnuYO8oPP0m3cNSt0/471nhHRGepULB2nLJtkMSmiX8aTzxfskoXH7FQ9STxu/Wsba88L800F3zTGn9qAUrr66uilYxLcuLndBAEuaTvIx11rgFWKX2azEmEeDrgWit76R+ibbR6hurPq9NtC14n9ra2prIDz2RFjHaouMpySDuTYzGBNHyEcBp99RRkrxJdIt1Pzm1W/qUSCl1gKhhGicgilrcALlLvqQJa1mUkfyUt4xXReKbK72IMMjwz/mNpi8R3lFXs8RXZN8+Yi1HlC0Pji3hWxLGErDNzFUkHrOiWc1arIg2dki8H9EzxS+npOVo/4/p0LumPc5p5edtl5QHtjFyOmcw0G8OltJqa5jyiXz1ej2xzweDwaa2Rw0RrEfarqTtj763JpUtbRniNpa2Haf1DZ17eApO8mxiO5H3xcVFE/Bx/jyZKhkyHnCNkGRwaiTJDm488NipUvmdpQ4QNUzj3DLTlJnnieBeJl6G9F06WqkJK5qfLxRuaWvKi7cXFdLExMSmI+6SMOj35ZupJQ+CBaik49RaPs1St2JNUGl4j6vycUcryjq+DmDfKo39hOVId/TgeE1PT4cBkTYveRskBTwYjMYFaSCKK0w81YRlcUud9hm9nDMyB7E8ysf6+jpMTk4Oj3ZL4I4rQW2u4HYDvfhR6i/NI8fnELYd26jNYb62aHyPNW4auOdp0cuBZUp9QI+i4/yLvEmmgWk6VhT8egBLKlvyqHkeIj5/KU90G3PXrl0wMTExvI+Lz6/otpXVDxrRNS/JwZIdhyggk/JK/Fkgt82najpA1DCNO6jamjzaZOVeJkux0u8orLwYHJqfCloOoiwrT2qn9jSFBmgka05KqwEqal1Lit/KR6167OfoXr/WflqudPeRxI93USJVzKiceH8hEOVbZppQ6/f7ooLX0lAFzOejF3fFPUOUP8o3CtKFhYURUM+Bk0YSH3wteHFIWr8NBps9JdI4SSCYl4njGTFc6PrDvpFuW6fzmMoAb80iUNMCg/H71NTUMJ6GB6xb5WvtioCeNsoBsMEGBXt8LDWZTYFhVHZF2iuBNstolNoZPSBCKbId7p0SzOwwZKkDRA3TuACRZpFGFrYkQL0tE/QQRawt+rvmSrWsZ41nzULW+saz5rS6tP7N5Iv0rZbH69OsAI/ykY07stqD3h9pm4aOpXRPDZ0XXPhxXiwPG1dC0rhIdUgkgVvuDdS2SiKWNAWukndEA8ESn5F0lDc6xyXPH/VWoNdVu6WZl0/fpeN5KJhFMB69YJD3ZQl4wTyWF9jrLymN5h3l88WTu3y9R73bEZIATdQ7l62XpqXzwZrD2tZ6VAeUUgeIGqZxbZlJi6OugKDla9ZkZLFErAfLstGUS7R9Utl1BKaXJ8OLV2bUY5Hls6RPSucQzSttO9DfrHiCiHsc54r2DpU3n6NrReK9xErW0lBgF1F8FviNADOLP66o6dq3PKZaeZpXifLkbcFKvEun5jJeA8wTbROmj3jHtLxeHktWarK3ZJ1qHs8IkI7wTIm2nW4Pa32hGRF0y7vbMtshNK7HXaXFQSeSZ9FS4vmsF6dRWFvAiAtVSXFY1hEFBZIHzLLqtbIjC34wsONXpP4CsO/d4bx4aSNX4Ef615obFqjm46CVG1XQ3lFvTwFwqzoDAiy+6FzKKBNurTZlqdJTRRGePCtamv9RJW6t16g3hZLU19H5Q/nhMkcDtVnwF/U4YLlRz3O2D3ibkKiX3YqZigI0ab1EZYJEXv18bDQPmqVP6NyOPBFSSh0gapi24h4iKx1OcC3+AEC+b0VLW6JMNcQfWUBSWgqYLMufl2m1C/NKr6x7dWggR+LFSou8eV44mrakf7W4G05WuXwMtDrp7yX3w/A6La9iZKykvoye1KTfMtsLEV5pvE3E+tVAufbwZcaLYI1DSXul+qV+90C1dMmoBC5KwF+mXXX6QpvPFmDCscU4zoxxKZG3zSz1qWVkaUar9F8LCEpXe0h1aldeNEEdIGqYttvTHRGLVrLWvEXmCdgI4s8I56w1oX3z9u0jiy1q3WZ4zwi2SDus/rW2J7x6qVCjVmakD0rAiSeUMzxnvlu8UuHdlEWNY1pq/UZBboYso6ZOWQCxbXRufFkApIRXuo4y44jl05ONHtjn92Xx+UzXpNRuGvPGgWVmq2sweOBkH16HoHkkKU/SnJWAIf6GwI3/d3V1VTQmUJ7g1re2C9DvP3BhbVZ+RagDRA3TdvEQbReK8Fu3TZk6slskJXy2Cfjq8FrSB00B20j6uuCnCYr0EVWk2aB763eA3KWUEj9N9lcJLxpF+oKnyXhiSuZLXcBHAcP+/fZzIfw7r08CH9F2e55vzjMGNWteJ6l9ljyR4gQ1DxGNC+QglIIiPGDAvaWDwSAdx5ahDhA1TOOKIcpakFtFEX7rtilTR0lAZJbPTHssy6vOGDdV7lbNt+1eb1vzejuN0TjGwKqjbVDcpCEW8RDR07YcbPBYuQzAjXi2qVcLvUOLi4uwb98+1WsVMYbozkMktop6yrRYIvRgzczMuBdSNk0dIGqYtquHqM1JVNeLMC4PUSaAsqSepjwwbXiISr0ImTbV4Vvid7vOV56uxBPUJG9t9VXJfC6to8mytyOQQu8HbgF521HRbW3JSyMR9TpJp/6i5fDyaBnoBdMugdXycUIvVsTj1TTtGED0ute9Dh71qEfBscceC3v37oWnPe1p8NnPfnYkzROf+MSRi8CqqoL//J//80iaL33pS3DuuefC7t27Ye/evfCSl7wE7r777pE0H/zgB+HMM8+EqakpOHDgAFxyySVhPrcqhsgTlpGJHlFK0m9ZKy8rUJoCVRkhk7XQvD7O9kM0fbQvpe2BDMiR2sbr5vMg0z5tDpUAFI/6/fzVBl55TXlSrHZ49UQBe7Yvo+3j68aTJ02PLZ2r1vjWrbdkvKlnhveNtB4x/fLyslku8iLdMs/bwj051DOklePpFcqzdwmsls/itS1Qq9GOAUSrq6twySWXwDXXXANXXXUVnHvuuXDaaafBt7/97WGaJz7xibC2tgY333zz8B9t2D333AOLi4tw9tlnw5VXXgkHDx6E+fl5eOUrXzlMc8MNN8Ds7Cy86EUvgmuvvRbe+MY3wuTkJFx66aUhPrcKEGlCnloGnhDgC11a+NJvWSGeFSiR9JE0UUDU78u3zXp1R4M+IzxH02eUFRd+ke1Dq20eALJ442NRV/lklXaTR3eb9ARljYto3sh3LV0GdNN148mTpseWzlXtGoYm6i0Zb3rxZIT/aJwM8kKvQ5CAJ53zvV5vE9jSDB+8N2hlZUWt3wLBJWQBora9fzsGEHH6+te/DlVVweWXXz787YlPfCK88IUvVPMcPHgQJiYm4JZbbhn+9uY3vxn27NkDd911FwAAvOxlL4NHPOIRI/me9axnqRPzzjvvhNtuu23476abbmodEGmeAkkIZCZQqYeohNe6ZbSVBtOVeIiydY/TQySVXXcbLOKR0IKPpVNRmF56eFKrQzq9Y1HUorYIeapzH43mcYvGgpSOR3T+l8gMTzlG5ItVhzdfJa9LST9F0kXnJY4j/1tqE50bmodLq5euJ64HEKhiUDcCoqmpKTGGh5aPN0svLCyI7eRlNwFULIM0a0xnaccCouuvvx6qqoJPfepTw9+e+MQnwvz8PPR6PXjEIx4Br3jFK+A73/nO8PurX/1qOOOMM0bKueGGG6CqKvjEJz4BAABPeMITNoGqt771rbBnzx6Rj4suumjTNl3bgKhJy6WjjiyqO6c0b5QEDKhwjXrneNxCFKRZHiINEHOe8a08r276e78/ekyaBqWiUtPul6L9qX3X6vUoA+q1e6LqkseD5OHdKpnntds7dYb5tQeOtTZJ9dL5SsEUPeFFgTY+TEzXmWZsLS4uwtTU1PARWgnUNulxlYwiSk2efJRoRwKie++9F57ylKfA4x73uJHf3/KWt8Cll14Kn/zkJ+HP/uzP4JRTToFnPOMZw+9ra2twzjnnjOT5zne+A1VVwcGDBwEA4PTTT4fXve51I2ne9773QVVV8N3vfncTL1vhIao7KTKCtA1vTpaHKI1DOJbWUepFGhd/GtVVeJ51L3kYMoHvPG4hwi8HJbwezUKlWxr9fh/W1tZGXrzHcrVta2wnD65FxUSVmgY2uPKLbpF58yLabxzcNjmvPX4lQNS2x0CjrIdIy0/vMiqtV+s3+jv/f9w6027lx3mKBj69XkC7Eb6JCxORD82Dq62xpmhHAqILL7wQ9u/fDzfddJOZ7rLLLoOqquDzn/88ALQDiDiNI4ZIE5RRRegJEW0hWSRZ/E3xQMu33OaeQM0CPU/4YJrI9hrNpwnLbF9HLUiaLxusiHWVbA95vNLvTd0PlRlXba56HiL8jQtnLahUsqrxbwREeEmeVR8SB2YRo8KbW6UeogiVzOuojNsqD5FG2TUWMQKiRgX/rq0LOv/RMOD19/t9mJiYgImJiZE39jgYobLM84pF+6/f7w9jrmhZTXujJNpxgOj5z38+PPjBD4YbbrjBTfvtb38bqqoaBkS3sWXGaRyAKGo9ZfJL3zPvNXnIXqrDKpsvYOmoqJVHsiQyQI9b8lIdWCb3JniCWxMcktDKvF3k9anEq9WH9DfcHooIuxIvRp37oSz+rbleqky1sUFwIz2JINUZ6SeJx6iHuGQda3XSb1pcmEbRW5zp91KDJUslZVjtKVljmgxBoIQAgHvm8BvGz3lrjY/d6urqMJ6I84tyil6OKM17emeQdHqthGjbaB9HDYE6tGMA0X333QfPf/7z4eSTT4bPfe5zoTwf/ehHoaoquPrqqwHggaDqr33ta8M0b3nLW2DPnj1w5513AsD9QdWLi4sj5Zx33nlhl+ZWnDIbDOK3d0YFgAYIvHJxUZacarPSaZZMhH/kRXtPR+oTCVBp9XBl4y1cTaBKFrIEFDSF7FnVEeuVlkHHFG+PtXimZUTfZbOAX1ZZ8TmbncNR0uatFCgu5ZVu6tXaKtWVOS2JcyizzWCtSzq2EePEeogZiRsI0XGnfRkFZ5qhlZkflieEAhnLMKHrSZMhGE+HdfHYLfw2Ozs7lDcRgwjzoEyVQLwGSrAcHHe8HoC+Xh+V61gPN2BoXJUnp5umHQOIfumXfgnm5ubgQx/60MixetzG+vznPw+vec1r4NChQ/DFL34R3vve98JDH/pQ+Imf+IlhGXjs/pxzzoGrrroKLr30Uti7d6947P6lL30pfOYzn4E/+IM/2HbH7iULI/IwHqaNuM6tkw4aL57Fm7HeeR2RQFhedsRLIVlCUrsy1ioHh1HBQIWzdnpKAk0S4EPi4+2BIq18zSuggbDl5WXo9XrDm3mjfSbxHrUI+TqwQK1XtzXm/NZhnkczCvg8jrSL8rG+vg7T09NwzDHHpIyf9fV1OProo9V7bXhbLcC+vLwMc3NzMD8/r3oCNjY2YHp6eqhopTHg7ZqdnYW1tbWUUYXKEz0UkRO2dL5SQ4uuB2+9a+NP67WA1mAw2BSQL6XRgBV+k26atuQ7nX8o77StcAp8qeyQDEsuTzLykstICvRwm65OeEiWdgwgkk5yVVU1vDTxy1/+MvzET/wEnHjiiTA9PQ0Pe9jD4KUvfemmht14443w5Cc/GXbv3g3z8/Pw4he/WLyY8ZGPfCRMTU3BQx/60G13MaOk5CKKUFOaUtnRrS8uYCyLN2M5SPVYAI2X7fHCy0WLSUof4TsDSiP5tTqldnmAj7cleseSBI64V8AC3lI9Upl87vC0UWCZAdxe3daYe31oCfs6R/5RiWaPOHMlI32P3A+E7fZOAaIHBQGYBMCl/se5hUAosgWEHg6pT6w5ZRku0fVuzQ/vYsjl5WWYmpoaPlJqzdWMPIpsp2prkJfT6/Vgfn5+WJbXn1Z93NuEZXHQi2Bzfn5eNRrq6BGPdgwg2im0VTFEVhrPYpHy0Yu+rHTaNpTEZx1k7+WN1GX9ph0lliwgrR+8Po4KPavOqEWm1SUJp0x/WuNNSbOiad9S4e2VZQn5jDKR2pGNYSkZA5qnxOrd2NiAubk5OProo4dHoKOEY6557CQ+Lc8EftOMKxz7paUltb187KnHg97VQ/tZAgbo4eF5abukYGNerlVPZn1Z6bEOlK0Yv7Nv377haTO+3SrJ7ijQj5BWFsbDebGREZBEAfnCwoI4Jrw8yztYR4941AGihmmrbqqmpAmOjFXpxSnUtaLaJG3BWPxwpeBZUFKZWhmR+iNlWuVIY25ZsHXGDpXZyspKqI5s32bakm2rNK+98iMCuHSeRPNbcStRiow79s++ffvCaT0ZEbHy+337qoN+f7Mnj/aj1T+RuuquF49onRjvSS9RxLgZ3u9Z2R0FC166yAEBrRxpnNAIs2QGgAyUNcOwDVDUAaKGaTt4iCzBES07cyuslj9zskUrt4R3zdVueQI0q91rQwZ8lQgrrlS0fvXGXAIl3ntC2jdUPmjtefmjwCfaFl5GNCZqMBgN2NT4yAIyj0eJD57Wy+/FrUQoMv8igeE8redFltos/S2No9dvSJYXMVIXr0fzGEVkglen5J3EOCDr6LxHmbQe0KpTr7deo7pL8lJJ6ZqkDhA1TFt1DxGlOui57kTD/NYJlEz9GX4si1QrK2PFZqgpC4YDNq1fsyBZ+y1C2YtBs32RFeyZ+RGxtusYFBEq7fe2lAClcSngtiz8Joh6c7hBs3//fjc2DsuIxjCWgm2tzmiwfskJO4m0daUB/2g4APVS1QGKGeoAUcO0HTxEW1l2xPuQqb9JAZ21zLeT0G66X7Xfdhq1ocDb7pfS8nfyeHEFPA5wJ/EQHX8pxghPZ0mn0vhJVVoXb2s0do6WLQEXDbhbJ4SpHLHiqLL9KvFIgRLKLXwsVgObUqxUJD6zKeoAUcO0HWKIkKLKvgRI8O/RbYts2U2mqVNeFJBE+bCONmtBrZm6PEsso1izFwE2peibLI/2R8mdNVaarCJpGxDVAU5Ngi7LuKmjgKP1carjmaMPmEpXT1gnVTlfmXgwy6tprRsNQOA2Z6/XG9nupG2UtkC9vtXCETBeCmOjtHuPrJilfr+v3oPWNHWAqGEaFyCKCC5JAFArwjp2bJXD6+YuZi7srPIjQqokjdU/mjvbskg8oeeVzYnG4fC+ko42e3Vx4vvv3GLNBGpGBbgXUyIRFZo8n9U+yYrXLHIsC0+6zM3NhfiMtIfO/ZKtigwAiSoGa91neJMoGqtjlRXxZHg8S+vWO70XBd7S3xRM8zg0bix5Hg3eh1Y7s9vTnF9epgaIrHUYmRfaWuUxaZoxYq21SP1NUQeIGqZxAaIISLCUKBXenrCUABA/lk49GhJA8gRbaYBvhEcrLU2H/4+LjwpqXJTULe4pHosHelJLApPS20IZJcctLppfe3NL69foo43oDl9ZWTHTUZIs08icoPnwtA6OjZRvY2Nj+C4TBTAWRQARVT5UIWtjw8FoZB3TuvAuomhAO603YlR4QDl6mssqi8ogS9FFDSlaHgcCkXbzcZZkJK0XgTWfr9xjFDU4NB5LgYA1b/F+obm5OfViUckD5gHxyO37nDhYteRMRCY0QR0gapi2wkOUsTIzaSNlaIo66w7PKIYsj9F03qLzLMkSHqS+kvJlx83iNSOwM+OC1/hLtyFbfJYoMWrR4m3I1HsntWNiYgImJyeHNwQ3tQUozRtLyWneh8jY8isPLK9UVkZEwK8U/0I9I5H6NEVo3coe5UOLQ7HazYN3OcClF2lKfcTHWpMp2ik6bfy1deq1iQMivvbpRZYScUCI/bpr165N6wbLlLxzHvF+i3iim9YTnDpA1DBtRQxR25PEoqjgq1POkUZtjWfEysqCPkolW2YWn1FAKilkrUwU7vypljqU9d5JHoXseEcUUUm5dCvXG3dU2BYQzdQ5PT0tvmGl1S1ti2bvWwMY9cTs379/k5ePv7wuASAL1Bx11FHDMngwsRU7o81va2sQgRf32NM2LS4uwtTUlHjBJ20L93hKDzxb3jStrzXQnL1luw3qAFHDNK5TZlr8RFPlZ8uT8rQJ1A43AJW15rW8FkUUdx3l3LY727LEM0QVYOncrDNeXEHVOd1TephBo8ilm7R8z3Mg9Y+mCJeWlkZisbLxQHXklgWqvcsC+XySvFfoPcVX4enYa6euKCijwFPbyqPXctB4IGmN820/Xg6tG/tEevcsY5Tw8r0x2QrZ3gGihmlc9xBFY3QolSrOSD5rIbUxsbPeiEgbsh6tOorMA48ZXqhlnI3Fyli7kTKi3p0S4gqxDqipy08dsF/HW8UV5VYbHNo4UN44n9pcl7bc6G/WVlCkLdLayPah5NGh6w/7g3tS8HeMxaHgSbpwk7aV5sU6NJDJj7dTPmka6YJSrU+jhwu8++cGg1g8Ypvz2qMOEDVMW+Ehik6gaLoSr8G4BDVSFhBFeMrwzUFphpr21FClZAkkLW9GSXi8a3mbnhNcCY2booDYUtr9/gNxTV6AO5ZD3xj0PC+UMqeVPKAheQWsODiPz+h8kw4KZC7apGsk6tHgJHl0NI+XFGAcPZjBT3x5fSb1MQWqmhdLuyGbtzlyuEAqh4P/iMzMzOumqQNEDVObgChqfUfSZayrrOCXyo4K2Tp1NJE+EuCMv0dPX0XqsXiPfLOs4CZ5i3xvy0OU4WMcVBdkSwrSK8dSlFZd6LWYnZ1114DHkwQwMkG1nryic5mOMT8dKQEHqz4adC2Bo8g8qjvvJDApecKsd8QkHjRgReOBaHu5AUUNqQxYktomGVdY39zc3HDbMNI/0rxuEyB1gKhhahMQUYvIO5LsBbrRSeZZsdadPd6CjAhrjedsHdl+kNrDBbvGd0aZceLWmpZGAz2R492R8iSKzh3pWoIMEIoCZOvkEvIh3RzstbXkfpcIj1oa+m19fR1mZ2dhfX09PC6Y5+lPf/oI33R+atsvCN5nZmY2eaK4csU+pfEqmpGwtrY29G5Za8xT3nwdSltLmG9iYgKmp6eHbZS8FxFjQgIF0fVkrU1vDmhyhoKg5eVl99Fcaf1JQI2vZ75Ni3FBNK6JgueMV9eSz7hGaIC6B6C0MWnq8IZEHSBqmNr2EGlbI5FFx8uKgBUtXQS1R0EXzU95ztYRKZMSzYuLjD9qqfGNAmxmZiYUx8L70bt9VuJZE27W5XcRJWTVLyk66R4jbaw1IamBSV4/zvPV1c131mA5kZfLucKQ4jysMeN/e+OpKSgAGJ7WwSsDtHtXaH5UJFNTU6qHSNvOwX6TxhLz8FuY6YkmbZsD+7CqKvXEEh0fOm+1LX/kZ2pqCqqqgl6vN3J4hJe3uroKi4uL6jH4CFChoMDqK8yDY8HjiLQ66fxAT83CwsLwJBgCWRw778SWtH7oONOxl8Dorl27YHJycgiC+NzlHiIPrPP+tIAiNUQiAEqquwNEO4jajiHii1i7C4Om9SZ0STrt/0vKjdRRh/cIf9lFpgEojaJCmoMeS7hYwMoDQBaQpOk1YGZ5iLy2Yn7puLYE+pAP7U6WyDYnKgJU7BEPEe8jzSCQlBGvj9L6+vrwPiTrkkOaH08pWbdsewG/kpLxgL0F7DY2NkbaIRkc1IOnGTvSOsSblPHJB67kcbsa5xHtx6ixR/uA3mclbQHS9YDtjd7Mz0GptF0leTot4nOOjj0/ns+97vRyz9XVB95oszzdpXKWjzPtJwn8WEZIhpdS6gBRwzQuQKQt2NJJUid/RNk3MYnbXAjZsi1FUSd9BhBaSt1T+NEtI00g1Ykjih5xjpTpfeM8R96I08q1xkb6W+oj2gfahYRSfppP63uPB0lR4/dInIjUz3SbTguoRf6l9mTmCAc6CCqk4+AWz5wk40brO/xNu+dHq1OaO9o6iMoiCUjiJZLUy8WBEfYXvdyTgisP2GgUMZQkI0EzNKL1NkkdIGqY2gZE3IIuEexWuSUTL2KRSYsly/e4F4dH2TZl+OcWYLQ8DTB7ebOAUPOoWeVEhWp2jDPj0KS7vQSgS+3Ltjma3hvjpuu1DCMrb7QfI4ZXtkwtLV9/WUDelIEZJawPPYn8uD16gmh7NIBqyRUPMFsya2Pj/mdD5ufni++saps6QNQwjcNDFLUQkSILrKmJZ1l+noLnVFdpZ/jLpsF0mTZxy9ADqF6ckXWvihQMa7XRmyN83nFg4QExa96WpKMkxTRppPFdcjov8nZbRMBnYzaiYMB7SgP7Ouo18/pK8+BYXi2AfHxbZIyywFdaD3WudeDgLetNLpVzUrtRTtFtMWl7tYn6rbwI1qTtVUra4YC6vEWoA0QN07guZpS8RJpSsxC4RE1ZX54yyNSD/2/dlhopJyJ8PXAg3USbUfKaBRbZUgCw3wzTlJLX59JWFU3LA3Y1BdLr9cRA4YjV6YEqLV9EeWnKHPNqz09YcxYDYRcWFtT6Im2hMSSZx2S1tkp9rfW/FvRseUMkI0DqJx4rkxlPfgQ8exowG+OnAeVSGcjXIY9f0xS+VEb2WgBpHHiMEF/P0XbVIQxGn5mZMcvHdFpcWpu7BB0gapjGdTGjJGwjkzkyobw00UnZVDk0rfWejlWPFlgaUdSc9gdOKPH68WV2LbiVC02L6CkXiyTlpVnOlhJFd7t3NwpVrh6wkOrUgD4nDdSWnEzRFIbHo6esM23BI+b06QqN6FzS0klKUQo8x3bzoGcLwHDwaQE/rDcTt8X5z641XoYHSJGyHqWM7OL9j23yPCVYhzQe0nqKgiFrXJoGHJSniCE5GAyg1+sNTxhK39sCbAAdIGqcxvm4qzWpLAvPy+Mttshi9PgAyN+eu7q6+YhtpB605q2Yl0xQaTaIE3nne/iW0rLaZPWb5dEA0C1nj+/IfUua4o3OxYj3w0pngRBL4Vlz2/tbo0zQOg+C9drueZI4jxrY1byHmcBtD/hFFKwlO6y1ZuXH36PbxtHxQu/OgQMHzCsTLBm7vr4+4iHy+F9eXt4EXiJjKo2xljfSZ9Y4abeVc4PMk4XUQMC+bRsEUeoAUcO0Fa/dRwRg3TyZ79EJnLVGola31D7PhR7hxbMiJQFAedFuX5X6K9ufmGZ1VT6eLqWLxBppQrWJMZaAiGXRc8GZCSiPAs4ovxJZ/HnAMHPruaWYonV7bYmutUyfaKQp96z3mKfNyKxofdS7o3lzs/xYdeM37iXSxjQSCC7Jowg/kvyj/En3gXHZ0e+PesslWchlaFZP1KEOEDVMWwGIMsI3kicrQLmyiU7gqFLlCzjiBpfaxy2TLC8eIPIsMqpcIsqdgpVoe1dXN19gKBEFD9ZDwRHBa5HVr5JCshQw7X9vPlqC3uqjKL8SWSDWy1u6NSS1LRuULlEUYEt5svXy9e0FYWv5Ih4N7XvU6FheXoZerwdLS0ubwLX3PpoWPC/xgWXQSyM9b1d2e1KTVxqfUuwi5TfiHea6QkoXkUFtUQeIGqZxAaLsYrbyc8oi8hJAlqGS8qU0mgCIUqk1TJWLF/ROKQISpDIy/V8CZuv2YynPVjB5pr6Mktf4zX738tZ9SsSbY6WUOcFXVw5o3pBofW2QZ7gAjF7sGIll1AL4MQ3tA9oXVnv7/XgMIgU8kmGg8Rnddm6T2q6nA0QN07gAkScM6oCk7BHkSL5s2XUAn2SFlJTZ9OLL1tlE/U31HS2LjqF0YsayeL12Wd8kgVwypouLi+YDk1Z7pT7ygLCUhpaR6Rttay2zBjPzqd/fHNNBy6P9wEEd95hZHo7IBY/ohaBPXkT61wOwvBzNQND6D+fl4uKiCWoHA//JGu5linrNIu3FdHgwBcdU6gPpEIU1f5oAp5H52TYI7gBRw7RdPETaxPEmdSReRCuDejT44qSWT+SItLcXbimA1VX9SGlkGypibTcBqDLjVlq/tbWlCVGJB1oW9VjRmAopmBP70LJ0aVus9ktjTtNH8uLcwOPyVh9KHgtsM7XGtXrp7xzMUYs+6gWgvPMTht682djYgOnp6ZSSw3yah4ivM7r+pXHisSOUrCPZdBywDL7Fy9cqrQ/7nLcV5yn1hNBxwTbQk1GaoWVtx1PyZKe0JqTvkbGTCAEuPspL+4uW74UHRNuVoYjsa6IeizpA1DCN6+kOSSloMSqUPLendCogGuSGwmNlZWWT+5byrVlJSByYScICy5Dczyio+RFxKiSt+0mwfultIS6Are0sz7rMCDn+uzaOHFxkrtK3QCaW1ev1Rl5NR8t+fn5+OKY4xrt27Rq+TG5Z87yfPO+e1q/0FXlrTA8cOAAzMzOwtLRkjo+21vh9S1q/0TL5eOFveIJI8npwooCo1+uNnMDyFAW920V7CFcyfHAcsV/p2CA/MzMzsLGxMTIXpIMFq6urwzfKeBzY4uLipqPovF+5h4iOB30sFecYPY0nyTEEe9RbwucfB/falQcaHxrxvra8a1J6WmfmwAN+46/clz4fEqkzSm2DnQh1gKhhGsfTHajEqFJCIOJ5hThYkISgJPToo4Ga+5YCM0nYc0GrgSJPWCBP+CI2b4umfKmVjIBIUkBcEVAXOAef0lX5UjskpW8JTt4WbgFrAcb0d+neD1oOHWtMy71yXAmhYp2dnd3UZ6iAMO3MzIwZKKz1gwTwJcXN3f3Im1Qf95Dw8qPxEbQfKWjm/WeVQQnrpUBBo8Hg/i0zBKTSnVwa0cdhtYBWyRChHhT6IjvmoW2mBodm7NB+pvXhrd/0dXvPoED+qddZuiNJMw54+7Rxo55R7/oOCpokPrR1KBmiHjiQ8mG7oocwqHeyDkXqpHxvNfDRqANEDdM4PET4wvTk5OSI4NC8FVywcCFmHXnlAsxy7UuWP3dx07SWAtH4B9CViNcWbu1ZAmEwGIgKh7dR256Txo2DUisPF1h8bDVhv7y8bI6XNUckrxzfHrKCf/m4eDEVdH5QBaMpDclCpn2ED1tqFyViXVTpct4lQKLNddo3XBlHKQOIkJfIo6acogqWe1Uo4ETQIr2ThaAwErdDH7WlYIMbGNFrKiifEqiXDD+az+tHCtw97w+mpdtn3jqUwFqUpDZG71SzjLK6HiItfyZIfyuoA0QN0zhiiPjWgDd5rclqCQrNutHySfU2segsAegtQo9/S3hETv5QhRAVHtqWntTmqHDjiiU7XlYfR9uHeSgo89JblwxGec4ogJJrF7RvHAyUWL2ZNnh8NkFRTxmljHcAYPSKgaiBQdeiVR83FCJ8ReJlEFBHTnFxPjy5kxlTKy1vr1duv68HzHsGlEda3+PYcw8zUt3TlnWpA0QN07hiiJoQiKVlNcVD28I9ykMW0NSlNhb9dujLEl7a4tsCMSWKqA6/EV7aqjubj89NDyhygyfSVulkIq1fehFd8hBp291ZIwBjHml8l1QubWvpSVxMT71C3slAHtMUPeiheTZp/2jbrpiOb9VrFDXSPdlngeVxUAeIGqZxxBA1deyw1Apoiocm21KHh4zl11SdTdzjo9E4BEndOtrmMaI4osKekuRNiFji1v010QdIrXmT8RxoNBhsju2z8kreHC09/93qE+/RUWn8JHDjgTnJq4o8WO/eTU5Oum/78Xw8PS3LiuNB+WS9g8j7x/qNt02KM/TKkIgGp0t8RQEkBVZboR86QNQw7QQPEZ+kPEDUs6iie+68LM571lOS9eZEFiMvs7QOTBv1TEhBlJHtF8kq54RxGPxxRN4fmoUdiQOJxn9p/eGB8agg1QS6NC6rq6vDO4gmJibU7UWrHahwFxcXh14OC1hZ3hBUTriNZwFk5H9ubk68nJJ7UPicjihRBFw0yNhas9LWb9QzoK0b65ACb6sUdE+3YC2FaoFLDRDRPMhnxJDq9/viCb+MfPK2elGGe0YWbVvG+yTVSevRTnhimRT0WfNRiwEbF3WAqGHaiqc7kKITiE98nIiep0Sa3Fo6qayolWjxLZXrKV2JXytPxmMUbZPU5xyUYr1WXA21GLXnNugRa/qdB5JL/YJKzrqaoN+3L+ujlwdq/eF5abSx4+VTF7vWx/hf7De0ZCmw9+YiV8K0j5eXl1XQQ/nj7UdlhMrECjTF+vHkHr2HSFJs1hzW+t5T0IPB6BZLFGhR/qP9a8kwyZCSeLfkoWWMRY2aTGydZ8RYea0xof0WMVJ4LJb1bI9GGbkt8S/lRx0UeUS6TeoAUcO0lYCoDsKPegYiVrtWVtSbYvEtlespXe1eHS1P2x4iSlSg4T8e0yDxx+9iodYXvreECporG34HCeWJAqJer6f2kSZ8qTWMHketL62+Rr65N5KWj21AcCN5PHi/zM3NDeNTeLyCxyvOJfQ0YR9TMMv7Q/OMIBhBRWbdXkyVu+Sd5R4Nry3SnIvMdZwbeO+QRtLa0kBMSVB6BjxZZUQeQc5+06g0jwdy+LzU1hId68z1H5G2lMhxSR5v5Sv3SB0gaph2gofocKNxCag2SAOnpQJGEvQWUJDK2rdvnxvbYgE/9OBo92IhRQC85PWJeKAi7fYuw9P4QUAWBSCUeNyQtdURbVvklBTNZ22TWHMvWo+kMCXFTr12dLte4sG7U0urV2oLv0BSAlZW33tgSqLMPOX10EtArfKteYSAXdom9vrRI2nrVCNtC1maI9gmL5SjSeoAUcO0lYDIo+hEz3pJtHqiAYAl4MRym9PFHvFmRdJZ/DdBTZ08Q8FiuZ6j45MJ4NR4ifS9NQ8iZWS2Lqz6IvVTwCjFYXgkeXNKQCfnwSsDPUscpEqg0Qvczl4RoIG+weD+G6onJibEJ0AoeKBByZiX9zvvWwnsra4+cGEoPt0ibdtKssQDeJG+oO1H7ya9MV3KY104StNqPPG+wnLRE80NHwn4ekCZbh9bhF5Z60Zy3s8czJVecRGhDhA1TOMARKUKmQoIqwxqUUQXPM9v3QDLBVXEMueEt+YuLCyI5eL/R+Kdsje2lvDrjRm2R3rLKVo+3X6QhKPnMeFptHaWtD9CpeW2mS/SR6X305RSv7/ZS+Xxj7E/09PT4YB5yXPifefpJiYmVAt/MHggJmnXrl0wPz+/6XkQTNfr9UYUriTL+P1XHAhQjxQ9yeQpWB6I3JSxgBfsTk9Pm3lK70PTeMG+02IVuRfTkgVYjxTbJvXL+vr6Jg+RNJb04Aff7svI6ix1gKhhGgcgKlUAdIFY7m/8XhIEiPkR2WuBt9wSyHqk8NbclZUVsdysF6RND1HEosT2UMspStyS0gCvZDHzNJLwryP02/YQldTTZNnWKTKtnEi7rTKil2XSdUxP13kGEebbt2/fJk8j9SBJnhUkTDczM2MeEMCHRhEUScqO1in1Gc5rb+tLAkjZixs9YOAZIvj/WObk5KT49l6WpG0rDcx7cVuUx8hdSQA+aJPGkNdHDTlpHngyvQnqAFHDNG4PUYlyXl1ddZ8KiCxurx6+dWPltYSaVr7UBxFBz4MLJYpuYUUWKRcGXoCppdgl1zcKLm+vPdImKli94/neOHljysGXJKg9D41FtH84Hx5IigrdaFwNBxJ0vKI3e0tGAOebAgauXOjcl/pE4pcfwefzT1OuyA+2DY0ryYNBAd7a2tqm49vSmETXv7WuSoC9lUdaO3wOW2uiFNjTuqVj/RpA9EjTASU0GAxGvOCSDKJrpO6lm6W0YwDR6173OnjUox4Fxx57LOzduxee9rSnwWc/+9mRNN/73vfgec97Hpx44olwzDHHwDOf+Uy45ZZbRtJ86UtfgnPPPRd2794Ne/fuhZe85CVw9913j6T54Ac/CGeeeSZMTU3BgQMH4JJLLgnzOe4Yoqy3qN/3H2DEdBKizy4oGuvgWVdUeVmLWuKVCxxJgaLy4sGFEtETSBZhfZYbl7chWrZUF/UCZUFkZOwkRapZ7ZGypDFF4pY3fRfM2trV6uZpef9QPrQyIuOptcEi2q/ofUEFRteIRdI2sdQn3BiRlLPUJ1JfeuAU248P5mrl0LVn9am1Nrx1jmmkR1zreNRLwDEfj4gi99azJz89r/5gMHoggbaxrmEQkdM01kjaWvOMwsj416UtA0Tf+c53UulXV1fhkksugWuuuQauuuoqOPfcc+G0006Db3/728M0F154IZx66qlw2WWXwaFDh2B5eRke+9jHDr/fc889sLi4CGeffTZceeWVcPDgQZifn4dXvvKVwzQ33HADzM7Owote9CK49tpr4Y1vfCNMTk7CpZdeGuJzKz1EpXvNmTRZlK55AaLIXxMsGZ5QSC4sLIQfZzxw4ADMzMxscmdLFqglOCTe+NHpqGXIt0qyVlLWIsY2SUe9rbK0PpL6hnoYpP+PlI/EhaTFh1QG5yfahqhVT/+fexMiW2DRu3M0Y0biNxPML7VVijeR0kbkTqSN0vjwPuZejew6QcqCY+/WZ488AyIyBy2gwA0q2kbNMIj2YdRA4rFglrzX+qdE9kWpVUD0b//tv4X/+3//76bfP/7xj8Ppp5+eLW6Evv71r0NVVXD55ZcDAMCtt94KRx99NLzzne8cpvnMZz4DVVXBFVdcAQAABw8ehImJiRGv0Zvf/GbYs2cP3HXXXQAA8LKXvQwe8YhHjNT1rGc9y7XekLYyhkiyIDXBb/0WndwRdypXrtJijwrw7CK1eLWUKr3AT/KQRY7ccuG2uipfnBbpR15nW8KA806FVOaYsaYkJdKs4qwFGFW4Vt95yqTJwO2MIojyn0mDFPVwZesqKdc7OQog9xOfQ9JWb1QO0m9ZcBwx3iKUMTYy9UprJAr0vfkZqXd5eXmTQVoq09uiVgHRueeeCyeeeCK84x3vAACAe++9Fy666CI4+uij4YUvfGGaWUrXX389VFUFn/rUpwAA4LLLLoOqquBb3/rWSLrTTjsN3vCGNwAAwKtf/Wo444wzRr7fcMMNUFUVfOITnwAAgCc84QmbeHvrW98Ke/bsEfm488474bbbbhv+u+mmm1oFRFYQpxRjIAkmScFxT45mfUlWaERgYHoe54KLBa+mt7Z/KLiieTRrQvOmDAYDdZsC+3dpaUksfzCQX2jnbeagAONGOKihv2v75rytlpKNgAIvvSSkIts62CaMG9G2UaQ8HChnryGIWpbRdPxvq2+zCiyi+LXftHVqkWVZc/kQKTMai+atEf43bpfRl9AjClObQ9oWFpL0m1YWpQjIjQJhizCtd6N6SZnZwORSoDIYPHDgQ3ujLiqzrHncFLW+Zfb7v//7MDs7C+eddx6srKzAySefDH/zN39TUtSQ7r33XnjKU54Cj3vc44a/ve1tb4OpqalNaR/96EfDy172MgAAWFtbg3POOWfk+3e+8x2oqgoOHjwIAACnn346vO51rxtJ8773vQ+qqoLvfve7m8q/6KKLht4E+q8tQGTtsXsClKbjXgnNoqMLULNE+CLVFq3kCeHWnSWIaB4vDgrTSDEL/b5+bDlibWkgUxPG2H7J4qIAMGL9WsLAarPVj5GnUCIWP+e1zsk9zyL18kt8eRa0lc/iKctrNj6Nluspea8cD2BEyozw73kypL83NjY2PWFSouw9Iy/Co2aYRRW4Vp/lqeZlIy+R51I0Hjiv2CfaoZo6IFva+u33/XCFiGzhfZddc1EaSwzRK17xCti1axccffTR8A//8A+lxQzpwgsvhP3798NNN900/G2rANFWeIgy+9RaGs1as7w9XlneEfaIZez9jb/VjYMqsfatNBG+s2VE+eHtklzTVvpIP0TGuAmrrdSCtXiIrgGpLO5l9GLgIvzwNWx5MrW80n09Wh96W0fZPuFPw0TJUv7R+jNUUpaXp44ilsABNQC5gYK8eI9pa7xLBg8HRPz0XxZk01AADNnwTrpxsgAR5kdPOu4wtHX0vlVA9M1vfhOe+cxnwtzcHPzRH/0RnH/++XDMMcfAH/zBHxQxCwDw/Oc/Hx784AfDDTfcMPL7Vm2ZcWozhkia8KX7ulr6SFyQRh4/JcKkSUugSWHbVn1RsGeVu7q6+QhuljRLGYWXtx0WATYS6LLmX6Q/pfki8RKd66jEqHVeerJPGls+Vjy95MHA4/DabeQ0Td0j05rMwVNs0j1jEeLjVMcYqGtIZKjUYAIYfTJEe7KkjqEmzbXFxUXxVBm9ubzkgVcOVLAs7QmPjNFA/+brLbKDUIdaBUQnn3wyPO5xjxsBL+94xzvgxBNPhHPPPTdV1n333QfPf/7z4eSTT4bPfe5zm75jUPVf/uVfDn/77Gc/KwZVf+1rXxumectb3gJ79uyBO++8EwDuD6peXFwcKfu8884LBwe2CYgkIWIpT0s5aOkjQs5a8JETIlFLWvq9jpDMgKsmBGpE6Et5vG0vrx2oZCNvC2lEBZ60FegFTFPFGY3F8PiOjJ817pQXDnSs8ihw0vpF44MfW+fKB61qbLOlDCiQw3UqXX5K09DYk5K7XbStX9zeojE/GbDAeZHGNgo+rbwZxZlR2hZpcwvnNz6cPDU1Ndy25+VzL6L3VMtgIL/xh3Oe9yF+X15e3hSTaslcj09Lt0QPZlhGQVtAF6lVQPSa17wG7r333k2/33TTTXD22WenyvqlX/olmJubgw996ENw8803D//RbawLL7wQTjvtNPjABz4Ahw4dgpWVlZEAYzx2f84558BVV10Fl156Kezdu1c8dv/Sl74UPvOZz8Af/MEfbJtj91klX5I+ajVnYyk8ARUVYHXSZYVaXUtEs649hV7XQ9Sk0KgLSDMeIi+WoLRdEi+ZsjQFlFXSfGw9JaGBLwmU8HgeCVhJ4JTyKSki69JIHvNjKTKrX7w+joLPzPhIVEd+cX7o22t0fuNv9GZ6KR6Mxmn1+30zLcptmobPeW0OSeNL5yV/BojXHe0XS7dkQNaOBkSUvve979XJLgYuV1U1cmkiXsx4wgknwOzsLDzjGc+Am2++eaScG2+8EZ785CfD7t27YX5+Hl784heLFzM+8pGPhKmpKXjoQx+6rS5m5F6Y7AQpVaaeJemV3ZQSj9ahCcmSEw0RyljHdRdz20KhLi+lY1kXpNThuTStBvxK11G2PyNPh1g8Uq+CBKKWl5fN+BVaNsav8dgYWm5UftG1Wno5YGZ+ebIhI//W19eH3h/+iCn2Ed7Hg7c28/rx4denP/3pw1OvPM4M+wXB1dTUlBuLgzxj+QcOHFAP3aAXknoaeR81sZYskFUKwEqpVUB07733wmte8xo4+eSTYXJyEr7whS8AAMD/9//9f/DHf/zHeW53ALUNiNASwJeam1DAEcHhWbOR+up8jyiF+fl50wJBD4S0HVWXd80jUGeMogIkw6eWrgTkarxEhZZk6UaFa5N566TF79bWYEl5GYFfqiSsWDO6ZjLbVtIWjaXgNK9gv59/YNpTnlY/RfqQ9wlPi2Wg107a/qXbZjzGS+onzQNI0+M1INY2Nl8f09PTUFUVHH300WpwO9125eNo9U/WSJFkEQfD3oGdpqhVQPTrv/7r8NCHPhT+7M/+DHbv3j0ERO94xzuGN1UebjQuD9HKyorrei5VTBL4wQXiXSDoCXsLVGn5vbqpu1gLMkUhrVmBnoL1QIhUbgREWvVqykKz+D0+rXo1Xr1+kXiJWNNeH0t9apUbBUi83CwQ1v7WTgLx/vEEOk0fPUnpPdeg1RuJNfNAQKQea9ysOR7x5lplR0G/9w0J14j2ZiCdC5rXDvt8bm5u00k9qS1ra2swPT0NS0tL6pqLnPjk6+PAgQNQVRUcOHDAlLvRtaLJ6AxIsnRXKejPUquA6MCBA/D3f//3AABw7LHHDgHRZz7zGTj++OOzxe0IGtdbZhHvQYlQwb9LJnfke8kpIg9YeNsGkUUZVbAZEELbq4ExXi5VPB4gkpRUVABJWxfWmFs3jVt9YPWNJcT7ff+ZgQxAovxIp8aigpaC62h9GQ8SzRcJrrfS0DGl/40ABKmsCMjNUvYCTk51FWVUpkUug43Ug+sIY4wkzw/Ns/9fLzWU1kGJgYf180d3NVDtyV4vXWZ8+v0HDvWsr6+rYLbJ+cepVUA0MzMDN954IwCMAqJPf/rTcMwxx2SL2xE07sddkTSFXSowomAqwpNluYyDD6nuUnAn9WfE8rS262i5VMhp3gRNKWN9kf7KtoPWyR+ItPJ6INeytr14BUtYa4oWlYz08GmEbw2MeulLbgb2rHNtrCmPWnBstF6JuNGV6b+6ZfH+rLOF4hloAJuvmeAn/qJ18/UWOZ2lrRF+fF86Mu/Jr8XFRZidnYWlpSURsNN+5ltnWl/WmQeY1toi1PqySWoVEP3oj/4o/K//9b8AYBQQ/fqv/zo8/vGPzxa3I2irAFHGEq9b5rjyN1WGVFYdoFjSn56i4d6R0r36iGehpB00PT0BU0qa9VtShqTQPLDEb0TmVGcM2qCspS0B5gjPkfkTmatRfmlZ2pUO0rrVFLg3Hpx3z2tmXTORGZOIsRGV3ThGeHyfPhAcfV4HPVQYi2oZ0hHg2NR6KLm8t0lqFRC95z3vgbm5Obj44othdnYWfuu3fgt+4Rd+AaampuBv//Zvixje7jROQERRfB0vSh3rNuIdkazXUktZ+y3CEy62tbW14RZbiYWJ9Udug9byW3VubGzA3NwcTE9Pw/r6usuH1TeRORK17vBvL17GahffpsueJIr0Af5OhTitmypCz0Pk8ZXd8im1pHnf8y0Fr6wMYLDmdtRDU7LOMTiYX/opeT5KPeFc0XsgJyvfvDZK66mO95AGvUtvLGr14ykzTb5EQQj+7sWY1aE2PUKcWj92/+EPfxjOPvts2Lt3L+zevRse97jH1X7LbDvTOGOIaFxA5CSIRt6Es75nLcGSuBdeh3U6xuIJf6du2YhVJZWvPVYYze/1Nz2J4vERucQRvTFS4LnmUchaiVo5SJZnqY7Qi1jYtO6IhyPKF5YbOfUplZdVKFjf7OxsUexTSXqpv7IxUZH6PJBqkQUuJEDF/x8pAnBLPRRc1jSh6On8yRzgsOrmfWQZn96at3guBZJt0tjuITpSaFyASBIgpSg9ap3WsZKsheMJBl6HdzrGs8Soh6MEELXtIRoMBrC4uAgzMzMhD1H0lBEPCuX9YsXF0PnmzRNtHno3mZcKvUheLWbDs3yjHiLt1KfHa1ah0LfM2lAqUrvp+uT1N3UMuk5wtQVwLSBvpbXGoxTIUFljHQSJemdo+sgOQeZWaQqcLKOrxEMaiWXbKuoAUcO0lafMSpRK22g9oliyZWYXYdQa2qmUEdCe8mhijMbRp3XqiPRXVumV8rPd5p/Ubm3OaMotAjgj9Uoklad5s/Cb9hyRZCxg8LDmcYmAD00203z0JmuvL7y+0b5r4FvqJy1f1EMUXScZw2or1kTjgOj444+HE044IfTvcKStCqoupZIJnQlmbAOMeEK7qXqaXJhNL/ISpcPJA5aRcpuyXrPkzQGr3ibalU1XN080r+XxKrmCg5MWi6UBFGucSp5SKVn7ka1evp2seYtKwLTEw/LyMlRVJd7HF/UQecamJatLPOMStWFQl3rgmqDGAdH//J//c/jvv//3/w4nnHAC/OzP/iz87u/+Lvzu7/4u/OzP/iyccMIJwxfoDzfaaYCo7oT2Jq9VfunE96zEkvzS98gWZBSIZQW5xz8XsCVlZS3PaL/zdmdOkmnWtWdxc176/dH3n7w6sjzx9mdOdGVOA0lKzrvcVAMpOA6R7TlNKdM1YV1tQD0vuKXLt2Z57GPkgsHoXIh+o3xKgfnaNrMFsiUZyY/PZ0B5CeDR+grJOj1n8dKkUaOVXwoAm6BWt8ye+cxnwhvf+MZNv7/xjW+Epz3tadnidgS1DYjanpjZOusol1JrVysr6omIAoGI4rDKot/qgjhKUgxVSVnZ/o+COt5u7z4dSlIdWts4sODzgb4QTr9nY+0sAMPLxDlj1SGVZ/U1b5e1TYWnh/iVAnQc6HZQVNlKSt2bc/wOHw6AMJZvaWlJPF0m9YO0xjXQTfvKil304oUiWzyWF8qadx7Ynpyc3PTEh5Q3ygPWE419i66ZKFixgL63nYdpmg7op9QqIDrmmGPg+uuv3/T79ddf313MWEilSnS71JkpKwpcqNAujUPIfI+mLaknUrcEiMYBlKN1WODUG6PIPSRInqdFAxnZ05gZjw4HRpF5ISkzmsbzBNC20ZOnEQWuKRePx4gBwr0QXFniPMZHSSVvBe8HDfhoQAnbaIERS9lH5nwENGl8WusB+5xfiqiNf+ZZpOha0DxJ2try5lPkwIXUFuy75eXl4sMsEWoVEJ122mnw27/925t+/+3f/m047bTTssXtCDrSPERtlpUFFFvRN01TBNR51uB2IK0dJR4ijbLjXTo/mpyzEnnKLFJm1EIvTc/5iM5TT9labxBK5UW9wDxtxHCpcydZNMiaAxHvDjkL/EZAoVZONH4Qn/jgxoDUDt6HtE4c67m5uU233PM2DQb330lFjb5xeIcAWgZEl1xyCUxOTsJTn/pU+I3f+A34jd/4DXjqU58KRx11FFxyySUl/G572uq3zMZV7nYCH00rxjba1oR3hVLJMeXSfsqcEKR5pCc+PH6k8rIKQ6O672ZpdTfxLEeJos+Cn7rzuaTuaHlNG1sZYI0U8cR5eWg+6ToLbDONS4ryas23rJHk1el5kCJbbhToYxC5dncb7+uVlZVhejqmbcYPAYzh2P3HPvYx6Pf7cOaZZ8KZZ54J/X4fPvaxj5UUtSNonPcQtYGUo5Z9k/XXFdhZXqLCoMm+zZQZSeuliVqWET4sq4yXSf/e7zzxEW1n5A04KjS1eeTxk6VSq1VqU3RsaN5ovU3MZ1S4dZ5aaZsyc0EimkcDGBGvNJ8XHFTwLe8oUKcAg7fL+iaRdyeYB1QtwETBC/bh8vIy9Ho9mJubUx/hpiSFBYyDunuIGqad7iHSFgqPo2jykr2SI6BceElCKvLERMRLUadtNB89cWPlj6TlbYjEJmi8W7/X4SN7rF/zDln10wvuvPidtbU1mJiYgAMHDhR5E/n/owdsfX3dPKXE+4OnHwxiF3Gur6/D0UcfDcccc4z67EzU65bx+KFHY2JiYtNJqbY8PPzvyNzV8pcE+koAIwqStMszMR3fhsp4iHgcl9c/GnHwSMcxYhRba1sCpijjM6dNm5xbUWodEN17771w3XXXwUc+8hG4/PLLR/4djrTdj917C0dbDBy0WCAmY60MBgP3DR6Jd0uI4Df6RIfW7qynpY617Xk7aJ0Za1wqNyMgLfCUCTjN9o2XP+NBwjtdLJ7RQyQdxdeI3hdD+aH1akG7fFywfgQVVAHRWBqNcD6j5Swp+mifacBRGgNsJ7/ckLfPAmORbVfeh7y/I3JJaqfnSYuc+sPyvHuX+N+SzOL97q1VDfjUkUW8HG8cebusuiUwQw2I0gt1S43RDLUKiK644gp4yEMeAhMTE8MOx38TExNFDG93GjcgilpBSFkAoJWvCSJqUXpHRZEfepxXs3p5mZQvvtDwG/UQ9fvyjbVa+zQhXcfV7AkFWic/4sz7jv8/96RoyknzZPC00ukSXqd0Mkry6EQFOn/OwCqPxrJwQK3Vt76+DhMTEyMAx1s3eKPw1NTUyAkXfrsxHys63r1eD5aWlmBxcRH27dsHKysrIwpoY2MDer0ezM/Pm8pifX19CE6qqoKZmZnhkXIvtkd6tkHyqNH1gH2P3ivcpqOeEDpeksK3QInEB21HJCha21qha8175gTjVebn5035GZWNfI6XvtHmGSWlAIHz7QV2S/k8cCPpmexOQJPGaJRaBURnnHEG/MzP/Axce+218K1vfQtuvfXWkX+HI40bEEWtIKRsYKkFfCwrQhKykhLki15qh2elRqzrwWAw4jHixPnWtl+sRcktLYkHK/DRstx53dK4eBacxp8FiKTyV1dXxePAWh08n3bJnWSpSp4yni7qvqfKSZpvUj9sbGwM540UtxMZU/4QMAemGUGPAIDO+ch2hBQ/ZSlc2sfYPn4fDu1Pus55ELEEGrBfJyYmRuY7tkW6k0girf+xnyIeVp6WA27PINDWK+ePAr4I8MB+XFxcDL1MnwWRCHKlZ00sonNDa7smU6qqgl6vJ/aBZsRJ/d8WtQqIZmdnxXuIDmfaqR6irAWm5ZWsqMi+t2S10G/UQ8TBVsTr4lk1/NSE5taW+KN1WQKv39ffVaI8aBYt7S9pXDTB6PEngVRLIaBFLW3bSHnpaRvveDnNi94ZLkAzgCI636xyvP7wtoMkL5fGo/e3VJ42rtbc99YsrSOyJjWArfUdArTZ2VlRaWrHsyP9RcuZmZlxlT3vPwqQKACWwPXCwgL0ej3z1mcKcDyDia4T9DriOtAMPuSLGw/afKagGj2m9E013h/a/MB/2p1B0prFy1Kl7X3JCKXGVEbPlVKrgOgnf/In4f3vf38RYzuVtjKGKIKgLeCjeYKkrRFu2WdPP0jl8nyWi917sJHyJnmetHQR74zGn0fRNke8fVZZWd60vtCEKp0rFojw5o1F/b7+9IbHfylly6nbz9Fy6d/ZPtRuOrZ4j7QL0ywvL4veOQkE4HzYv38/rK2tiV4PnDfRk4UaeWtNSs+NjV6vp25DUwVP47qigF/6jn2FfYoeWO1wCBI1pCjw1eYK5ePAgQNQVRUsLi6O9K8HrCjY5t5inp+3jQNkDoQoyOJpojshpdQqIHrXu94FP/IjPwKXXHIJHDp0CK6++uqRf4cjbeXFjFEBLVmHGNcgKR8tVoAqOv5MAq2LCgHqidGEh2ZV0m8zMzPDAFXaFu6NQkuXW+gUqHl76Lz93jho3z2ByD0O3mVnlsCzrP8MaNba4fFgAeGIQvf6iqcrub22FEhp4+T1sQUerfxc+URi8/A3BBfT09OwtLTkbqfwsrw5Jm230bZSTwKN98ItMckAiayTzNZTJA09+eW1jbaRxz9aW8Ief9TQyAA6z4CxiBvCfD1pstHiVRofyeDWjFDLmNrRHiIeSI3B1F1QdTlZE77EAtUmJU/vnULQPEy8PnSV85M2Hn9YHy4UtKLoxWBcCWNeyaqgZXlWB42/KLXyvT6yyqD8lZYheRsylAEPlsLy6s+ClH7/AU9S9n6cqKeB8+P1KZ+zHrjUjAmJXy1AV1rHg8H928loPJR43Lw+0uK3tDlAPQUR0GH1RROeAixHGoOMYSD95vHI5VWJ8RLh08rHjYmovJCAlAf+pG00D5S3DYA4tQqIbrzxRvPf4Ug74emO7AKKWGRRi5MK0IjFzAUM3yqz2qItKs/C54QPZmJsT9YLh0SPcEdIElglgijSVq8cSclrZXmg3RKMPK/H18bGxlDha0fRM33B65PawkEAb5OnFOl3Gs+hKWPpN62fKMBAQwYNEIx18QARt/6j80L6xgGctxaj85L2eWTMNSWMf3vbUiUATGobl1eWF6hE1mdAlGQQanNZCoq3+suqr9RoaUL3edRdzNgwjSOGaBwTo836o0KI58E9+0xcigSYrO1BqTy6KCVBEBXqS0tLwyPckWBRWrcWVIhKRzr2TUlTzHRLIwJi6LYAClLJ5Z0BTBbYwj5bWVlR2yV5BaV2oKK3TjBFAJnl/uf5pD6gaREQYXC8BGwk8sA+lkXHLXoPTAQQRcElxpdMT0+bZdI+0QwQDUxYHl5prklb5dqbXbQtkcMb0jjQ/swAfj63Nb5o/qgHmc47zdtI+9NasxGKtMVrXwkozVLjgOi9730vfP/73x/+v/XvcKRxAKJxTIw268f8mQA5Dgysxavxiv9vXQSpAQdrUXrtofVaVwRYdWsKEhWDd+xaU0Le8VmpD7WAR63dEs+a8uB5p6enhx6OSLssBYNKQDv26+Xn5Ugn/CLzkoIJ7i3T5rnGV3Qt0rG2eOJeDE2B8zG3xhq3nNELJrWR1onXHOAJNIkXnIcLCwvuCT7u8aBACvuk1+uZwJvyZa1drIsabnSbMBL7xPvO8qrw9Ri95NZaC7zPAGSvqLdOrPK89BYI31Eeol27dsHXvva14f9r/7oYojKyJlfJhJHyRL0u0f1dzZqMBqNa3zyFKAl6K4g62nb6PXpcPnr0P+o9ot8yAkfKF912yHjISttDv6MX0btcL9peLVgzMw+o50PzzEXWIipRelJLW49asG5mrUpzJAK4JaAXBWz4TTr9pNW5vr4O09PTcODAAXVrlV+TYQFCbZvZ8hDx9iCAoMf4JX7Qo4nAidaxuqpfO1EKAjjA80650XmDgFC7302K3bT62QLtmQMWUZDfBnVbZg3TVgZVl0wkScBlrM5MOs8jlA0YboInTzE2WScnTdmUCIK61lOdMc96DTL1labXKNrnXn0bG6MXNpaMPfUmWPfMcI9Upi+8NeV5ayS+vXkmpeGeDM8g6vftS0RxrmmeTQrGtdg9C+jzfuNlUJCwuvrAhaPoBZ6amtr0vh71HEme7uhVIvz3aCykJPNWV1dhampKvIuIjkVku0ublxqgstKXzLEmqANEDdNWBlWXTBJJmUXLyabzFmwTgKiU93GAEa/OaKCipnBKg73x94jXzKqbxzllgl213625E/WOaF6MaJulPqAeouyRf64gNA8j1il5LyyPIOVV845Ynh3LWIjMdUmBck+GpJj50zyS95JuP1nzGAPtZ2dnhzGDPA9tJ5/DPM6Q9yMFMPQKERxLGsxOPVD8dB1dM9qWnARapXGy5oH1N3q1tMMeTegD6VspqNEAVhPUAaKGaTs87pqdaJ6ALS03myd7cqFJasvioGQpGul7tJwM/yVem6zgrQMued4SfiNlZYUqV55eILc1HprFrfEreUEsq523Mzs2Vp6I0aL1rQZu+/3Rh3Kt9kQ8XljW5OTkyNMg3IMpAT3N8ySBCeohkoC2FEfEQS1NY11h4PGv9YUVx+Zt+25XsuZ+XeoAUcO0HQBRVujVUcS8LC2PFbSLZXgKKusRKE3TRB6tHM+yjwLTSOyPltfyfFhWtxZ/onlXSnjj9WStTus7v/IhI1RpWZEAU08ZWf1mtZ32rbZevHzZNUR/i3pxvXnFv1vzSzu2rvWZtjaalg+Z+afNkWjMT5Y3Lz2NidJAXdNyskk52kQ5EnWAqGHaDoAoMmEsKzJTrgdkNOtG+u4pqIhHoLSMCKiLeBLqlhUFkJhOisvKjGfUSyLxHY1PifZbydhkiM6POhe+lXhZmqKSdcvz1UlX0i6pzAg/TaXZruR5xJtsGx83vvXIPWOavKblcRAblW2RdTPuCxmROkDUMO2EGKKIJyKK9iNAxqoPF4n2JlKG7wg48/jI5pOoxDvAv1t3JdF03j03Hp9RL4nEtweImrJom1IMGQBYwuc4KOpdiuYrTVeX54jia8qjs109E22Az2hdmmdNC/qWyuOvF9TVKZRPy9BrEyy1DojuvfdeuO666+AjH/kIXH755SP/DkfaCafMSiwvPpmjngyvPs+DlOW91DvjLdaMUs4CKInqBJhHQXNdgTsuYNBkPVsJZupSk17M7UJN8O3Nd4yV8YC/BzYjXhMOMiy+mgAQ0fQSmJBkevRaAOR/eXkZFhcXYWZmBiYmJhoxWrBc3jfIn/RYcVPUKiC64oor4CEPecjw/bLuHqL61JSHKGtVeQCp6frqpvPyNilspO91BX1birsNq7QtXpseo7byNlVWCSivYzVv1Rqum4eTZyRGlahWTtT44/ktvjSDhwKlqJGYMSopT/T/JYBG5xZe+Cid1MN7l/Aizbqk9XcG3JZSq4DojDPOgJ/5mZ+Ba6+9Fr71rW/BrbfeOvLvcKTtEEPUBnnWU/T30nqapqYt6jqAMZu+juLxnhehW3VRJdjWEdiMF5Kmb9p7qJFWXulc0HjgY0fHMMJ3lM8S3mgdJfOgifWe9Yxky4nymLnNWQNE/X7s9nieh951FPU60f+X+MFy6TUC/NmbjY0N6PV6MD8/74Y7RMbBA4Rt64dWAdHs7Cxcf/31RYztVNoJj7s2UaZnlVjuV6veusAiUo9kDdWlOt4ngNw9QtnTeFYeDoToA6ARnug4140HkfLwo8mcqBKKeljQJU9vDJfyegrSOyiA+SRlQ9N4N50j3xhnt7S0pIJWiWft7h6vfdb2Cs/f7+sPL1vzonT+REBQtJ0Z+WQR97hYdWrvOJbIJj4OkaB7Xo80R3Ferq2twcLCAqysrKjvnmUPgWjprflQV8ZGqFVA9JM/+ZPw/ve/v4ixnUpbGUM0zjKthRBRZFq9fKJnvQRSObyeOn0YWYhNehuk/siepLPyYFrJJR7hyepnStH7aySAbLV3/7++k8Uvs7PKxyPH9N4bC4hoYD96f4tlfff7fbcNyB/eaE0fSuUk8Yz1W4/aSmVIga2UJwqyJdCdLYf+1xsLHEP6npyW1hpHCzxo+TSgx9eCNEe0PsmAOO9gibYe6TfsPxoUrQFpa95IxoEFnrHPtEMjlvFGZUF0OzFLrQKid73rXfAjP/IjcMkll8ChQ4fg6quvHvl3ONKR5iHSrL7o3UNevZ41rpEl7Or0YQTsNDlGllCL1h+xpjMxKJ6ikMqIAKIIQOZUcpnnYDD6tla2PVRoR4C6tJVClZrnIeLlYDrJg2Pd6B29gyhy07gEVHl/UX4j9wxl7lbiCp2m4/cQedvEWr0e8I8APf78COZfW1uD2dlZWF9fH/ZnFMR5DzlrfUbLtAAlEvUsaoHOUl2WjKRvvmnyoKl1V0KtAiLtUdcuqLpZiii8JkBJtHxNkJTUjemiVlGWhyy/GXd2KTDK8L4VVMJfJM9WtbuOQWABdS2NZjB4YNtScBGg7pFlRHCKAFGPP28r0SK+FqW+jvYJBXcaeJLSRACm9pgz9wpGjRhNBmXleEQmUmApgTutbutvbPfCwkKxbGw63IFSq4DoxhtvNP8djjQuQMQRedTVzEkTzrQe7hrPCHHre0ZgWZ4Dq/1WPZoQ1YRjVulY6S2BVJqPp2tScGj1lijiSJ5suXVATTSPB7w54Vzq9XpivBIHSh4/vE8y8VOZ9lmeGqTsFqjEX52rJThJcqwErFuyoiRoXCuviWeKInyXEgez2hZxpF9oWevr6zA9PQ1LS0vFc7XptlLqLmZsmMYFiKgwsSZl1DsSDY7mk7HEexPlDUkSHtyatRalp8ypEMVHFvFop6SwottLpaDHEpYe+KPprPehsjxr9TYNRKQ+jtTRFjCrkx7npWZZZ5Ui75um4yiw/JWVFXU7haYp9e7Q9E2B9ixY1dI0Ia9K8pTwz2VgKSCW5mHUm8TlgteOfv+BY/orKytpXnl5TVPrgOjzn/88vOAFL4CzzjoLzjrrLPgv/+W/wOc///l0OZdffjk89alPHQYWvvvd7x75fsEFFww7Gv9xy+Mb3/gG9Pt9OO6442Bubg6e+9znwh133DGS5uqrr4bHP/7xMD09DQ9+8IPhN3/zN1N8jiuGSHoBu84k0fKXvNtUIogskhRRRpBleLA8RHXay6kU9EQtVVQ2XmC7VLdnIZfs30f7SOpbq94oSJXmS53TPNHvWj18HDPzWTs1FuHFU7CTk5MwPT2tAiKrbjp20biofn/z6bQseQaPNffb8jZ4/c//X1rTHm+lIBBgVP7w7btsO+n8tnjGtFNTU8Nts2i7xkWtAqJLL70Upqam4DGPeQz86q/+Kvzqr/4qPOYxj4Hp6Wn427/921RZBw8ehFe96lXwrne9SwVET3rSk+Dmm28e/vvmN785kuZJT3oSnHHGGfCxj30MPvKRj8DDHvYwOO+884bfb7vtNjjppJPg/PPPh2uuuQbe/va3w+7du+Etb3lLmM9xnTJrK6hMq89TvpSXpgXROBdICZhr2jvhgZ5Mf5RY7Z6SLfFMRPtI6lvrZEm0XJ6uDUWYKZODXp7X2k4qDVCPKNj9/3rxHfWS0rlgxf1QUIpKdnZ21q1zdnbWvdjP81Jkg/wzaUrI63/+/5KXpeSyTU+u8PGhoEjzEGXeXIuMk/dMU1sgNUKtAqJHPvKR8PKXv3zT7y9/+cvhzDPPzBb3ACMKIHra056m5rn22muhqqqRgXr/+98Pu3btgq985SsAAPCmN70JTjjhBLjrrrtGeH34wx+ulnvnnXfCbbfdNvx30003bckps7oLW5v0JZ4Yb4FFytGsKa/NEYXepIBEq8c6gVHSvnEI89L8Ud6koNc6dWn9pAWuWjxrnpuStlp1ROci/9sCPaVzI5MvcvIKSVJg6+vrMDs7C2tra+46X15edp9+sJRudIyaWlPZftS8Qtq8jgKMLG+0XBwfPOUmpUWQhrsykgfJWkd1xqnNwGmLWgVE09PT8LnPfW7T79dddx1MT09ni3uAEQUQzc3Nwd69e+EHf/AH4cILL4T/9//+3/D7n/zJn8Dxxx8/kufuu++GyclJeNe73gUAAM9+9rM3gaoPfOADUFXVJm8T0kUXXbRpq24rbqqui6o1t6m1WDO8SL9FF69Wp/S7pUhKebf4xfQ83siikj6N8JpRgmglRhSe9s0bP/74Y9bqjQpQFNj79u0Llxk54u0FF3ttio5rKXhpm+oAjWjbUfF6dzrROppaL6VpMsHgJbxSg5LHipYADCRqPKysrIS8hZOTk6pHR5MhddreRN461CogevCDHwx/8Rd/sen3P//zP4dTTz01W9wDjAiA6O1vfzu8973vhU9+8pPw7ne/G374h38YHv3oR8M999wDAACvfe1r4Qd/8Ac3lbV3715405veBAAAP/VTPwW/+Iu/OPL905/+NFRVBddee63Iy7g9RBrVFaCahyiidCO8ZIWm5xnQ0vDYKo+vTBoL1NW5x6cJ74nGnwUmpTgla0z4N2/8pNiCDHCMCsXl5WWoqgqWl5fDZaIgl+K0vDTYHtzC09oUHdetEv5tUgZMZU9vNbVeStNkAFEJqKTzgdaVWX8aoeGLx96tO5o8L40mQ6Q2ZWS4lb5tahUQ/fqv/zocf/zxcPHFF8OHP/xh+PCHPwyvf/3r4fjjj4fXvOY1RQwDyICI0xe+8AWoqgr+/u//HgDaA0ScdsJbZt5ksyZmyUS1FkNTrlEUDk3GVlltr8N76ZHbzLjR3zifFu8WwOPlZ9pR6iFqelvL28qhdWq84imspaUl6Pf1Zxi8tmHZbc2HSLqSccnWb+XhYxw1pkraUDoHm/DeeWOgBdhb8yQyrvwb3yqzDKZs7KhFkXq2g2HQKiC677774A1veAOccsopQ/f5KaecAr/zO78D9913XxHDADFABAAwPz8Pf/iHfwgA7W2ZcdpuFzNK3+nEk/Ja3y0viVa/t40VOUbu1dOU8tQsNZ7GOk5dpz+s/FHQ5413ZBtIC5yO9E8T5PERzc/7yDtR43m9+v0+9Hq9kVMy1jzRxqkJRRDNZ60x6g1r+9oCKU/E6+HVE22Dli46TpSyoMhqQ1QGRuamJafp77gNpp0CjMSBer/Tb5qBpYHhErDXBLUGiO6++27Y2NiAW265BQAAbr/9drj99tvLuOSMBADRTTfdBLt27YL3vve9APBAUPWhQ4eGaf7mb/5GDKr+/ve/P0zzyle+0gyq5rQVgCgqMCQB4AEcKy+Sp+Cto7qegubtiGxhWOV4YNDas6dlTExMwPT0NKysrKhKRhOi1rYezc+BT6SdUn/y8YxcYhmpu03BFAV/Xn7eR543JqIcuTLJXs+AsSGaVyTar5l0kXipbD+XKC2prRjXoj3xER2zjY0NM8CetzVyn5M2NlGgHunfJsY7Iqfp71agNID//p5Vd51vkf4tNSAi1KqHaPfu3Y3dSH3HHXfAlVdeCVdeeSVUVQVveMMb4Morr4QvfelLcMcdd8BLXvISuOKKK+CLX/wi/P3f/z386I/+KJx++ulw5513Dst40pOeBGeeeSZ8/OMfh49+9KNw+umnjxy7v/XWW+Gkk06CZz/72XDNNdfAO97xDpidnd1Wx+4l8hZdRnBJf3tXxfPFoyF/Psmjd5XQMqV7dSLKU+ojaWHhdoh2aZjFB0/D+8zjk/MY8dJw8gCPJyg95Y7tsjxxlgLzhD/2XfTdpIzVqo1LlHh+TzBzPhGULCwsmO9IaYqi5DSjxEvmWyl5hgcl7A+8n4b3S0QBRhQ950Naj94cyQL1usA+SpE1QckDmRIg8k4iR7bZNaMAKSPL2+jLVgHRE5/4xNDWVoQ++MEPiqe5LrjgAvjud78L55xzDuzduxeOPvpo2L9/P6ytrQ29U0jf+MY34LzzzoNjjz0W9uzZA895znPMixlPOeUUuPjii1N8bmUMURPIWSrD+00SNNJ2Ek/nbV9IfEkLJbJALGuE5vO8XVEBF63PylOy8EuFRUa5e2mtcfXyanNHKyMz5yNlR8qIAkPeZwhUEXRrfEhKGXnXHjStM09K5qqXxjOYaL6lpSWoqgqOOeaYTe2Lglhsw+LionqcnI+dBC7pHCmVNVI/oHHTNjACyK1ljaQ0nnzyvg8GA9i3bx/s2rUL9u3bJ+oPzzBrm1oFRH/+538OD33oQ+GNb3wj/OM//mP32n2DJL1j1MSTEpIAksq3BBX9ZvG0sbEBCwsLI9tOGcs+I5yifeRZ4prFo6WLjEnGwopQidC2lE62zTzImKaPbH9kgmyjc42WHR1brYx+P3ZTvGY0DAby3UkWSOG8e55Or00UoEn9ppWZAcZIHBDxfHRLnY9PtA4OPKy+wPq0+D8KdusCC61M73JCr53eHLcAZMaIsOrOAqR+/4FnO2j4hMRPKY91qXvtvmEaFyCiVjgVbpoSoeRZuZIgl+5koeVoSkuKl7GEKi/TuiBO+51bGxkrB9NYVqLWl5ri8bYOLJ4iiinLEy/D25qLxlhovNLfo7xh2ZH4Mpz/s7Ozodu9I3xa+QF8JS/lkXimW5vWXOGKzlrD0pqVYsL2/+ut1JQHDzQi3wsLC7C4uOjG69B1SGN3NNDLx1x7wNYC5Vp/aH2pUVSOekpbGlcvhicqK3jayBzOGGmWgeQZcFK/Ly8vQ6/XCxnBkd+apu61+4Zp3B4iDESkMS3eQkWBgO5LywWPgh/L58JTU5jIA9ZBBTlNLy0amgaFI69XEua0Xq0fLOHL+4dbidYijcT7RMFNCYiSyvMs3CiYs8BLhFdJSHq8YdmR0zfWfCjl08ovpc0Ka2nuWYqm35cvurTWHl07UjqJB14Pbxvm0bbvtP7iv1mgVHvSRPt/a/1ovGhj4gEuLX20XCQvzs4LgtdkXmYOW7xa69Mqx6tjdXXV3S62+I+eSC6l7rX7hmncMUSS0oosVJxc3g2x1J3tWQwSUJBc+1HvgKUcrMdPvX6gi95bYBI4lOKLvD63rNKIcrXSZIBI1OKy+M3UYeXzys+cCKvT/5F0vHzNc+HxSf/W7i/S1jR6bqj3RuKXrx3qkeHrifehZMFzfrA87kWKzDdve5MftIg8fSGBVckD5Z30oiAkCqIkyhwW4TxE5ZKUrwSYcaJjHZVXkW/4O16iSgPn6dz2DqtEPMZ1qFVAtLGxYf47HGkcgKiOEspaQBElotXRRHl1+NJ4KQFmHiDy8lp1RsrNWMQSkMi02ePJ69eosPLS1/1ekr7fjwVe8y3rDJ/4937nhmvN+8P5y9TvWfeWxzjqcfT6NxJ3ovUNJ00WSLxY/KHnAhV1Rq5ywrnhPWzLKbtGJarDN8/f7/eHD/16+tqr1zKscd5hfFHUQG2DWgVExx9//Mi/Y445Bnbt2gXT09NwwgknFDG83WkcgKjUemlDgWTz1LG8suVoaTKLygIiJXklpRIBRJ5FTPmSTnlFlVqEpyb61UofsegxXQZgRz0FkTIp6MzebYTpn/70p5sPbGpeFh6QnfFQeVs1Vts1frJGlgXmo2OPlJmLFn9ZY8eiqIeI86N5tjLKPypfsWzrlvXBYACzs7Ohk8BevVr/Uu9Q5EqJHQ2IJPrc5z4HZ511Flx66aVNFLftaNweotJvWQ9RxHUt1a8pg4iw0II7Nb54OyO3o1r9ixe98S2Ekv7Gti8sLMDS0tJI/ZqS8u790IS9dnrPCpa1+oDX5b2UrfEZ7at+f/QElDVeUcAqAUrJEm9KCVnlYHpUNrOzs+HxkACxl57OeQ8Q83H3gInVdutUqja/sqCqKQUZBVDZ+iLzgM5DOvejxku0PkoIUGZmZkzAE31WxttC1sY7a3A2ZVBrtCUxRIPBIHX7806itgGRN/G8CYOCydqrpUSDqnHxZvba6aKn9Xou8X5fv3eFWhtS/RZP/f4DRz81axDzlwaPUqJjpLnStTK4pyciXLEMbpFhf0rbIVHAwfnyFDnPS4U9z8cFIw0ItrawokCB1o1WsfTid4mnQFuHXpzb+vo6TE9Pm5c08v6xYjto+XyNZk9N4vrk6zQCcmkZVLnTcaRjRWVaROGVKEVP2UZlabZuKz3WSYEPHbuSrbMsIFpcXAwBHo9oO7U283nJ+bXy4TxpIuTCoi0BRFdeeSUcd9xxTRW3rahtQESVpCS8vQVBgYYXtDcYPHCRFi6clZWV8BF/bpnSerN3xtD0tN1ZK462SVN6fAFKPGT6mwp+yUOktYFvjUSEK/K4srJi3h6OhLET0VMfyNfMzAxMTEyYioFbl5JA1NLyU5SRu4O8cd//ryfR8Hg+Vzh8btTxPmjCn4+B1/+0vKghw8GYB3p5PXQeWRa9VwZ6EicmJob107VEjSQqj7xtI1q+td3j8e0pY8k70pQHy0rjzeOovLHy1blNvrTNlpGAZVjxYNG4sjrUKiB673vfO/LvPe95D7z5zW+GRzziEfCkJz2piOHtTuP0EFmBahppk86zctGKyVgtHLjUQfeUl8wtppZAjYCyrDKoU4YnoCOCKjsnUCH3er3Q2GTK522w5gBPSz0i3Iq28lmEIGXXrl1ioCgX2Nnx9hQbGiPSTcyRrUxPoXA+2rjtN6osB4PB8EkOOrckTwK/pNDzAGM5CKQkTyXefr20tAQA/kED6aLM6Phba1zrL7qOvLgt+t0CbtpbcFJb+PqSAIjFfwl5ZWkGkwfSm6SxXsw4MTEBJ510Epx33nnw1a9+tYjh7U7jPHaPC0Fy/WfJU8AlC6PJQEXLu2DxZllMWnla3iaEQ5TX0rqyXkP8Fo1XyIxpKRiU+NKAeIk1Hp1H2THwFGgEEFoGR5a/UlCdbbdmTKFHmG6Ta7GItCzLS0fHb3l5eeiB4n2OsTFTU1Pq/MbxiNzHZvWLJSe0OYGGyMzMjOmx7/dHt/glHnDXYHJyMnQxKQXhksfR4j8zN0rWTwTwN6lXOHX3EDVM4wJEVDBkvDYRpVFahpYuguyjglqypjOKFimy+NoGQNm0mT6K9k+Wzzp9Eg3QLK1PAwBW3EJdqms08HIygbQRAGZZ/6XlRtLTeYjjbl2cScuy1iX/roGqxcVFmJmZgaWlJRNoYnnefWy8ndFx10AV7ZOFhYURYELTrK6uQq/XM72y6+vrMDk5Cbt27VI96Bzk4M3uWL+2HS3Jksi2l3enkNU31rj2+/LrB03RWADRXXfdBZ/97Gfh7rvvLi1ix1CbgEia1J6A4fmbCNjLCG0AOcjSs7J4e2k6HhRdAiSy1rZVVqTtXIhGyuS/R5QBwGYLShI0dd3PUeElXcwZsWBLeeLxJ5oijoJLb45k1mHdOqX2ZY0b7xs3OOqsHc6755EFyF3GqZG15krXPU+bBY0eb1LZ2F8IWKxtY3pfkMQbD1+g60KLN9S8R9Y49vvxO4WsPpS+4W8luitKrQKi73znO/Cc5zxn6Mr7whe+AAAAL3jBC+D1r399ntsdQG0CIk+5eosb3bT79u2D1dXVkHKlv/P4kajHh1sNEQU1GNwfg8AtgY2NjaE7nFsIWhsiVo0nrC0waPV75ASNBnSkMaJ8agLCAkRU2PJAxYzCpv3A20X5jAYp0z6IxKtJgAvLoXxp1r8nXKU5Ia0/HF9r20EqLwpaeL9I2ysScOaB/Na1EahssXz6PIjUtmjcGe9j75SQtw6jMs8CVSsrK1BVFaysrIg8R8qg9UYMigjYl9LwuWXlpQBH4kmTCTg/pBOpVVWJa8fqc+kQRFPAPQNeS6lVQPTLv/zL8GM/9mPwkY98BI455pghIHrPe94Dj3zkI/Pc7gAal4dIIk/xS1YCVa6S8qDClS8qCrAkECIFxa6urppCD9uJ++Ic+HBrSKqTl8sFhgeYLGXmBSFK9WI+KXBTSocgAoNNte0eKYaMKjg+hvg7vQSNe3A84IjCk7rveX/ReYHBrcvLy2J5tAxsK7abx8ZZwI6moVs1Hoi1rG7t7b6IopLmP42j8axs7Rg2zg86lyVASvPiOtLqokfyeQCvNB9wfL3TcTgW0XcWLXnC+4Xm4UaT1R8Y6L2wsCDyjIT9tn//fjOd1Z4m0kS3mTUw6QE3bZ5TWRHZnqL9ZQH07U6tAqLTTjsNrrjiCgAAOPbYY4eA6Prrr++O3dckDUFnLCxJIXkPO0qKTxKKXOFQIBEFdpp1orXRszii+fgCjgAFT9hw61srgyqbiFeF960kfPhYcBAR6SOsDy1HS0BSJWUFQGqKS/MQcaCgCXpLIWr9nrFerfnJ82Nb6Emq7HzSALrlEUDKeIgifYDpozcLW8aGJE+y12lwr7VULp0PJRcNWn3iyTKtf/nvWn3eXKE8WGtBMuQGg81Xe3CyvM1Sf+FVCNRYsLyCkT60+rFpahUQ7d69ewiCKCC66qqrYM+ePdnidgSNCxDhRF9eXh4ReJHJpV3uyCcdt7b5hKTCkVrk1kmSiKDx7siQFnFEgEZvWMYL85aWlsw+5YLIunUX+fZuvV5dXYUDBw7AzMyMyydXdtL4SfxZ/Frubk8o0fowqHVtbS0FRvj4WgpUKy9zP41GEaDAtzSl8qW0dC2VHjSgoCgSEAzg36sT6V/Pu8vL1drHy+L9HfUqcOApgfxSRSr1h8VXJj01Ji2vudXnXtu1NPQ3790+C1xKxAFcZByjaSJvDNalVgHRE57wBPi93/s9ALgfEN1www0AcH8MkeeC26k0bg8R3sWhucQlQvfmwsJCyCOApxe0CYkTGj0PmgeC1s1d0XRRaAsEhYPEh5SH/xZ1g9NtBssyo+3GUxveMX5rUaMwmZycNPmkAo16k3h7sTyNLyk9Hz/Lw8N5onOJPkvh5eMCV7vR2CtD84DROqKKFl/lttYVBSWeAuD9440N9oVWFgI/7OeIUuv1esN1LylKyZtAgQ1vq0WWZwK/S4oT01ug1mqjVE/2BCqS5LGyACyti6bnRgbAA/ML47Asj6NWN/WOZbww2PcLCwuwuLhoeogyfSZ5IyOAO2Kk1AG2GWoVEH3kIx+BY489Fi688EKYmZmBF77whfBTP/VTcMwxx8ChQ4eKGN7uNO63zLg3I5IXLXC8dZovRL6YNQ8R54d6HrS0EQ+R9v90IXMPhiRAJQ9NxPOAC3t+fn4kpkXjeWNjYwhMtfgHXNCWABoMBkOlNT097bqxsT1YL+93rrAtD5Y0jshzxGVOFRz1EFleLsn6pYJPao+l9FCZWoHWUQ8R9uvMzIzaX1IbPK8OnXMewPD6enZ2dvhYtuR15KAH59bKyooJfiSQhG2MbN1oZVlt80CtVFdEQVrj4YF9+l0CVry9modLMoQksCXF60hgHvmh6aPAD/mMPqGUIc/g9EDydqDWj91//vOfh1/4hV+ARz/60fDDP/zDcP7558MnP/nJkqJ2BI37tfuoxSvl1YRWtkyvnjrEFS1fSJJXAyAWFxTxSPX7D3jhLM+SJ1w1PjlFgj65MOWeh6h3JGqZWf1I06FCsFz4vB2ZIFpvnCKKOvrdu9nYApYeRbwNXn4EUxEPkwQUM8AtyqsHGqS/OdFYFCudBDKy4+CtWcto4u211pYE3iRDVuLHktd0XLU4JQ3gasZDCVlGKU+HYKzEEBgHdRczNkzj8hBFYhC0vJKVW0fAR+spLQcVvhYYKC1sLkik+CdJ2EWVY6TPooqFpvPip6QyPP61djQBkChZFqCUL6KQIh6iKO9aniiA0xRSdu1J8RylYMqLQZK2a5qmDE+eoSQBAKtttF2ZvBkwoJUVXXcS0W15vv484Mp/09axNE+jQeUZ6vdjgd+rq6tDL+XMzIwJ5LN37DVFHSBqmNoGRDjZcQ96Zmamscm9lcjcoqzionnwv96dMzytV0ckXZNleenqCM7od6/OCFiyvAdt9Y1WnzcvvHKy85EDe+/4fWk7efq2g1E5T9l5Sr9lAqut+Wflzc7zbJ9zkkCIdfovU59kHFuA3fPKWvVY4+Zt/+I8RBBovYVI10pbc1ajVgARvltm/ZucnKzF+HaltgERLhYaTC09buiRNMGb9hhk0kTySgIwEkzobeVI+/lYnxc3ZYGBaP4osPCEKLequMUcOeVG69U8OBYgsHiPWKa8z6LzS/OEWEpSG58oEFxbWwudWgSQj+prlrDlkYgGz0bi+ShFPJPanOGnPrNrAevnF65GgIp1PYc2tnyuZIGX1V+8/weD0buPrHXPy1hfXzevTMA+sILieblUb0RPJ9K+tra5LBBjrVGJ6uiMOtQKIHrPe96j/nv5y18Ou3fvhunp6VqMb1ca12v36+vrsLy8DDMzMzAxMZG2cqUJrlnQES+IVT9XzFE+vUWG1o5l/fK6pa1GTfFnrGutr7K/e3k0fjY2NjY9dMnBIFpoUW8BeiHppYqSp8MC1ZH2Sm307jqS+JHis7y5J/Hl8Yrf6Um6CGDjoFxbC3X6EMd53759Ka8GDYjNyIHoGqHp6LzBuqiijgIinPP8RCgF7NpN73SueIBP+qb1F8pVGttFb8eWAIzWb9K2msVfBkBqD9pq5Mliq692Eo1ty+yzn/0sPP3pT4fJyUn4uZ/7ObjxxhvrFLdtaVweIq7gPfe95IWwFJuURytH8xZIeTxLg7fVukE64v7V4mYsgUitPMuqzVh7PI2VV7Iy8XeNH81apoA1aqFh/fyNIyxPuync453+rsVSra6uwtzcnAjEpPToql9YWHA9RFI/R+600njHY+94LYU2DzQPpNb3nlfRijHBunBLInp8nXof+VrW+gPrj3igNFmF85PfpwYQ2+KlCh5lGZVr/MCBdUBDIg20ULnCZTLdPrJOSnpzIXuCWOt3TeZQvugdbZ4sk+ZfpD+xzroxbW3EQSG1Doi+8pWvwC/8wi/A0UcfDU996lPhU5/6VEkxO4bGFUOE/416XqSFzcGM5eXxgE9mS8QDYlJbpTZYglprd2QBRyzxCD/RcrO/S6SNT4nVRkGUBkK8/X2vTZY3hx4Pt8qTFGKEJ2wXgoeId0PjmfeHxKcXPB7pNy2NNwepJyNat7cuM/NSoiigyqx/Ov+jN2OX8OoZPfxvT0ZbPHkALtKe6FhRbyet1+MLj/7TYGnruhDtyZfM2NCrRpqm1gDRrbfeCi972ctg9+7dsLKyAh/+8IeLmdxJNM57iKLHaGkeC+xIHgEkqnw0D0UUnKEQ1JRYVPBIPGqgL3MaLyq4uLDMuJRLBG2kHzIKQOsbK1YpWge/FZzXZQl4fP+s1+uZYBfnEQpj9BRa/UbnXfSWd/yde1ski1dSjpE4pcyapmk97ww/xm7dHJ31IGo8ZuettW5p2ojRFVGsWWBEwVY2WJz+LgFjC3wMBoPh1hbdAuX8RAxZax0BwCYPkRcPhN+RP1yDGvjGtmAavla1vpX4pZdaRscwSq0Aot/8zd+EE088EX7kR34E3vOe99RicKfROO8hoguhrtUGYFuykUWS4cUSBJH8JdZWnf7x8ke273h5pUdLo23x0uF37bXrktgpHAN+d1Om/yOxYbR+HnsirQ+anr4vx09HWUHO3PuA/WS9Pq8pe76WpG01bxvc6lMOxDCt5OWiZWnfrf6PxmBpv0ugsQkZpwG3KADjfPP5ZJUp1Y3eFP7uGp8LEoiieTg/mXAJrw81I8lLt7Gx4T5eLT0zQ9sigXttrLjMaopaO2U2OzsLP/3TPw3PeMYz1H+HI43TQ0RvKrUWdNQi0qxZrxzPColazVY9/LeocIwucCuvZa0jZbZFsGwJYGYtXKsvvTmB+bhnUPMWSOPIhSCOC73de2NjA3q9HszPz5uXtkXaFEmL3yWPJ/V+SXFO0phQoU0FMdbNPZ10bkreNgl4UcVnbSlH5iS2g4Ja7cJDSbFFFKE2J6w0mW1qCRRG5ZhUJv1/qRwPXEr1WmVKdVMAY8VGeiAhMgYRPq1+8LZNvb7h9Wtbf3T9SsBOKz8rb6PUCiC64IIL4Od//ufdf4cjjestM4D4pMgs9qwVFsnDv0etFE1gWUrQWjgR4Z1pl8ezl05SFNn+17wppUKSl809R3wcpduCcWywXZH39jLtjvAu8U/7SzvFJo0P3SqTLHZLQUl9JAWw0jtkrC0UzTLmdUpH/ClIkwKCIxRJL7XJ82RpW3VZEKQpfAs8WmDNGlvP4NBAuwa8eV7P8xMlTe5JcwnnxsrKSmgLKzMfNPkmgTCvn7PzIkrdxYwN0zgBUYkS5qRZZpnJ533ne/8ZZS3VQS0Ob2FSpZjJF+GzJK0lEDwFL5G2VVcCOvk3/raSpBwsNzduv0gniDL8WGkz44dKIAqIeDvo71RZeXf4cIWPgc7S48rW+sO5LPEeBf70qDrd+rP63QIZ3vhTYKY9veNtO3rjSn/nJ7y8MjLbkrz8jDLmSt+L5cL+8mLEOEXvk9IMAw+cRPSFRVJ665mcfj//PFUd6gBRwzSuU2alpw608uoIDa+c7IT2rDzpht+IZShZZk1ZGlFvXdPWjqUcSsGcJBQj5fK5md1+yZKmkDVCQKwd0cc0HPx4DwdHTnLRcqkVHokjo+OhxWlI80/q337/gWsTvPfCpPr5PJFizeg8kB6U5Wn5tqO0TrW+pHMAvWt4B5AEZLNznHvT+B1DUdKMDKlttE812amtHQq2pbGnW7+SIcUPQ0j9HTGKMqTNC5xDdcvPUAeIGqZx3UMkBT82jaQtoaGljVjqJTygcIjcI9IWLxa1tac9DspYfVbfWoIzUq9FmnLPAHXJ0rUCZb16LMAU4T86Fy0LPds/kbK0+rlhFFGGqHh7vV44Loyu80hafuEjjsfa2towbooDLMuTwmUOB1+Li4siKNXGgRLKicXFRbF+Hm+nlafNXwtse0B8MBiIl4168oACuKaN8qZ1mkcdIGqYxuEhWl1dhQMHDsDMzMwIko9Y79mJGkXnTdct5fUsxybqKy1HU7xt8Viav45i5mkla9FStlZ6ry7PirQEqndyzANx9IK8bLxXSX9r3wcD+SLJSN1RPppYxzSdFpMSnUeRdnEZ1e/3h89D4faWtFVuxWphmRzsegDAmxOYHj1nExMTm7aKpBu9tT7TvPeSrETetC1s3m/amrD6v2kA05Qsj1IHiBqmccQQ0f1fy01PqXSiNjHBm1wkbS0Qqdw2+6xun2zleEbKaqI/uXVeYkV6INoDXQCjTyhY2zASSZ5Dr10aH1oAfYSifd/WereAQ515IRH3dGjhBZFtOe6J9wBAVD7hXTpchvMbw/k1D3zeeHFSUjwSjUWTDgd4oD+77jPfs+mapg4QNUzjBEQTExOhhyUlK0pKo1ml3sLzyqvrlcjwW0qSxej1SbQv67a/aS9DyXhm6miqHZnbz6021RXC1lF9j7zYngh4wL/xwsr5+Xmz/jqen0hflXhwtCscrPtrSusHiF/iqJ1ws+ZUtr+0fIuLi5u8/Bz0aEHiWYDLgTyeMPPevJOuayiRvZgnenKuScMtQx0gapjGdQ+Rt3UUsXwpeYK5CSuuTnmahemd2vDKob9HT5BE4oVK25rxGFhlSPOjyf6PkpdP4yMyx3n5WyVELfLASaRfsV2RI/c0vQWypPIjsYLRcaFlW48K9/ubL9a02qbxVAqEqdct2n7c7qJv7VmkgQFvnBCwSTf6R+YQ5qcn/LT/akCZbjeWeIV42yN9LJVZKn+y1AGihmlcMUTeFfvc0vAmv3f0kS4uz8qMlleiFDgAwoUmubalMi3hLwl7aWFKislSSpH+p/xFPFWegpMeYJWOZtd95JKSNEc88GiNk/WILOal4zUuoVm3nihw42OkjRUvT1pblmWesd6l+SmVReeuJa8Ggweep5DmSKkxF81Lr62ItJ+GK/R6PTWdxAc/yu557Wk+K220/XS9RA/K1D3EkgHbFo3L2OkAUcM0jlNmkWcVsp6GiFWZyZ+xUqPKVhLammLXyvSEv9dmTSFYSinbzogFbpWlleHFs1gUBa18bpaevsP7cjC4U6uv9PmTOlRXOEcBVRNjk1FIdbwxWZ6jZbbtIZLAo6X8KSCijw9Hyi2RBZjPWkfR9tP1UnKVSqafpb6MGvNSuZ2HaIfSuDxEJfcytCV4pG+ZCRyNFcmk9eqP8ldHKETqzHp/6rRPmjtNCpo65VvCu+4YNkl11h8vx5tXTbSvbhnZ/FsxJhHKrBfP2IiMPy0juhYlfhBQWPcXlayxknHyjEwP8KHBRLdII2A+Ul9T1AGihmmcN1UD5CbHOIRjCWDITPwSj0NTC6i0HEv5eZZiFIRGBX7UM9YERZQHpuFxEk31tUYZEM7HqwmvFN2q0eaBB5LGAT4yHiaPIqCvjsFhpffWmQRg6hw88Nb4YJA75UbfBrR4z/JW2iavfgsE9nq9kZit6HUMpe3NUAeIGqZxA6KM67XETZqtS0tT4vKVlI93aifCU6lAyPS1ZS151qNWpyXkPd6w3+gx22zbsoQWoRWwSq1GL1bBoswcB3jgKH3k2go+BqVxSzQtP0IftbLp/G9LEUpKKdqvFnnt0dJYv0fryniISuvUSCs7EosVfQ8uCiJx/lqHR7Jz2osRw7IiJ9Y8cBUxsurQjgFEl19+OTz1qU8dPhT57ne/e+T7fffdB69+9athYWEBZmZm4KyzzoLPfe5zI2m+8Y1vQL/fh+OOOw7m5ubguc99Ltxxxx0jaa6++mp4/OMfD9PT0/DgBz8YfvM3fzPFZ5uASJoMkcmLi48+9qiRB1yyx/fxb+/K+ujitQSXpnR5njpxLZ5XIWJpRsZMyqsJ+cj19qXBzdL3KP8oKPFYteQB4ryXegAzwaIA5R4i2jb8VwJu6VH+zFrCE069Xi99shLXIAfFEeWrrWtrrVn9Jq1FS75YfSuNZZTfSL9poIGf4Mr2RYYfTT56842CLipPMT4PAQrtPw+s8TrQk+XFVnpGyGAgXzxKyyiV3VHaMYDo4MGD8KpXvQre9a53iYDo4osvhrm5OXjPe94DV199Nfz0T/80POQhD4Hvfe97wzRPetKT4IwzzoCPfexj8JGPfAQe9rCHwXnnnTf8ftttt8FJJ50E559/PlxzzTXw9re/HXbv3g1vectbwny2CYioNV2qMDxrx5twpZaap6i0ciO8Z63Y0kUVERRUSNWxMD1LyUtr8VVCtI5IfTx9yWO8Gd7aCK62+gz5p1sZXr/UnRt0/WuHKiyDAU9z8btnqFKLbo9FPDlSGklJ110vEW9fnbWolYfeTwkMcLmlGTMRgKmBxMh84zzQU2PoXJiYmBjpP25gLC8vQ6/XE8E7BdOSnKbfvfum6PzWTtl2gEggDojuu+8+WFhYgN/6rd8a/nbrrbfC9PQ0vP3tbwcAgGuvvRaqqhoZ0Pe///2wa9cu+MpXvgIAAG9605vghBNOgLvuumuY5uUvfzk8/OEPD/PWtodIu4vEyiN5Kaz0lrehicvystZOiXUaSZe1sL1HZS2+M+3LtAW/Rd3IWT6kOiIeKdpnUtq6Yxjlm1LGKwRgg/QSDxHltWRrEC1oTTlpPCO/uK2heSwzD2lq48RPFUnzWtuyiZSp9QlVtNK6q/vgMP8dPXxLS0vDCxYRHGFaCpK4R5CPkwUwtVPF2bXB5QQ9Mcevt8B8NA0a4tr65OVnwS6O5czMzMjpUms8M+snQocFIPrCF74AVVXBlVdeOZLuJ37iJ+CXf/mXAQDgT/7kT+D4448f+X733XfD5OQkvOtd7wIAgGc/+9nwtKc9bSTNBz7wAaiqCr75zW+KvNx5551w2223Df/ddNNNY3ntPjoRSi3RzPH8tsooKTuTrqQc3v/j6o822lJSR+bYboYvjbIWobU+MnFDVlnWfPCo7bkf4bm0fKutmXZRD4RlWGCZ6LmSypbmB+UlAjy44o5cq0F5Q68G3YaSrgGx3kDTwOPqqv2YLM3rAUcEOJOTk9Dr9aDX68HS0pIagkHrRxCO+bF/rBgwvh0aAZq8/6W5oo1jE3RYAKJ/+Id/gKqq4Ktf/epIup/5mZ+B//Af/gMAALz2ta+FH/zBH9xU1t69e+FNb3oTAAD81E/9FPziL/7iyPdPf/rTUFUVXHvttSIvF1100QiCxn/bBRBl03t5olZJ5EhpKbqP5vf4iHoMrHQeL56wsniULCPNoo6OmZXOayc9DWbFa2lWYgllAZElKNfX12F2dhbW1tZqraE6azA6X7RxzASUZuqN5LHARMRi155A0ZSeFIRLaX19fbjdox2y4PxJfNJ2ra+vjzxuqvU7LZfGytETVLxfefCxNyY4973nNSLAEdMsLy8P33mTLpf0gvb5WPHDGjQeyTrIQQGaFhdJAZznTWyKOkBUExCN20OUtcSyQrCJcjTvQRNAK5vGEuLRkxueVZnti2jZXj/W4T/aV5jOClrmdWlPMZSA6ezctNJHFQwvu2Q+U7LWbHQMKP/aScFIvRmQhNsleDWABH4ywfrooauqapO1Lx368GQdKvbJyUlzflAg740B8jg7O6vyIfUb9gMHRNr4rq6uurGRKysrUFUVLC0thQwja8sTweXKygpMTk4OvVpzc3Mj+WlIhjY/pDmEa0TKL60fCtAkI4z2UxvAR6PDAhBt5ZYZp7aP3WfiIKKC2BM8USvdE9jWJI8APZpGW6y8HOsJEcqHpZgkReAJSautlivZAx8l/GtzhpdjPQ1hWWk8jkZ7iiE6xpGb2L0yJc8ACussoPDGwepjyotUX0TZIpUCOm2deeuaxo9IAfEINKz31VZXV4enmtbW1sSTdevr6zA9PQ1zc3Opu6jQ47e+vm7KA1T+COysfoqcWJPmHPZVr9czQR0FaN67iRyQanXz9lrB7giY5ubmRl675yfFsidj8fvKyop6LYoUX1bX2GiaDgtAhEHVv/3bvz387bbbbhODqg8dOjRM8zd/8zdiUPX3v//9YZpXvvKV2yaoGqA5D1EEXCBlAZFnLXpek0h7PEVoCRBPUUTSePVEFJvUTxr4sNro1e/1lRcP5IFPK9AyUg5PU7otJPGG2wjSSSCvnKgSoJ6FaHxStA4pbVMXBkbWq+RdofNG61M6N9CTo/UN9Rz1ej0zXkYja457B1GissTyxGiGAAdYOFd27dplvtOHaelDspG1oQFz+juVMXzLjxppdGw9uRSZm5YsbitIOks7BhDdcccdcOWVV8KVV14JVVXBG97wBrjyyivhS1/6EgDcf+z++OOPh/e+973wyU9+Ep72tKeJx+7PPPNM+PjHPw4f/ehH4fTTTx85dn/rrbfCSSedBM9+9rPhmmuugXe84x0wOzu7bY7dA5TFBXjlNJW26XReGRHF2aQStspqqj9L+6ZOmW3U2US+JuZ39HRRVClq5Hlu64DEkrSRvHXHwSqT9rfXNzS2CI+CU/CUNZa8b97fSBIosK4UkDzkPB2WSQOwNVk+GAxgZmZm6CWKGMOehwjHptfrwfz8PKysrIjxRwjc6AWk1hZfhDdrHmllN6ErMrRjANEHP/hBMXj5ggsuAIAHLmY86aSTYHp6Gs466yy47rrrRsr4xje+Aeeddx4ce+yxsGfPHnjOc55jXsx4yimnwMUXX5zicxw3VdP94BLL/nChyCKk5HkTouVEyqxLdcvdTmPPAafV35Rvb1yyx+h5+d7vnhLw8tLfNG9MZu5pHrkI0XqycyOTPtMeqVxri9tSltFxjY6plJbOYwq0+W98C5nWTwPFadm8jVg39SRF2milQU8Qep2qqoLp6ekR3i1wGznFVjKnLO9TnTlfQjsGEO0UGgcgQhdzr9czFYLlorQWj0Wl1lqJJVx6Z4hGdQSrVl9dMKV5puqUq+WPWsYAzT2ii7xIR469Ez/ePCjZpsoCEO0SQa9M/psGiKJznab1tjitvBGgyUlKr82PzByLgjTtm3ciiqfBOjXla8kt7RZvnNtVVQ2DwiWwJAFjuv75bdHLy8vhG8mxTd5LBP1+f8SZMDExMXJ3ktbvkcMb2TvdOGjU5GCdOV9CHSBqmNq+mLHf78Pa2hrMzs7C0tJS2oKmgqMEfUese+tUQcRCxQU+OzsbuqE6Y5V4W2NW++qCDKm8OpeuWSTll5SD1lYEGwsLC+5YeXEB2ukXCSREQedg4F9SGO2XaFpNmUYMAK/eaJweLas07iLjWdPWTBSMRuSFBdKsfrMOJ0hppPKiQFEDHdg/GD9E17K0vvHk2MrKykj/8C0r3DrErTLpJmzke2NjYxinZR1GGAwGI+XiPNCCuyP6AdNowfWcV+nkHC0Hea8jV+tQB4gapraf7qBAgU5USch5CocuZu/mWEsIcyDEj1JyoeottMHg/kBIfmqD85PZd8bfqOVlWSSWxVQaOCi1QQrOzCps644j+rukHLi7H9PybVktcNry8khxCRo/Eevcq6NtoZmdc9EycV56gCgK0Dyy1p9Uh9S/0Zgpb25K2zEWSNHWkkaeJyvjqbC22rS1LHmIvKsMEOwjaJHqpv2igTVpTLUg79I5HZm/dJ7idt3CwoJZVwlAboI6QNQwte0h0hC9ZLF5bnr878TExKbLyADkRRexJPjpE55XEkackHd6xJjywwUL50d7OwstJM+asihq9UrjYOX1yubla8BDK0vizSuDjxUd46wb3eJHanekn62rEKJUAkK1NZCt2zMOeNrILb7RNnieXN5elBVeTItXDv0unQCjeWkAMI51RlF6sVu83y1PXVYZayDf8srw9mtrKQMMuRxvOowCwL/UFefp4uLi8KqEkvJK11mUOkDUMG3VPUTS75FJxReoZqllLAmeVvrbsh5pmRo/mqWl8Yp5lpeXR66it+rWBIQmNCJC2bNCM1t6tE0Rb5fVlmi8FoJdL15ByhsNyozyZJWRSRsRst6YZ+9twTR0vL08kjLNzkUvjXUPD92a2b9/vwlO+DzWABLeW0NPZvGx4XchSWtE4yUbu+VtsWXIqtsCRZQ3aysqSnxtWTKGg26NL94nHpDEOqOxQJm51SR1gKhh2qp7iKSFqwkhS3hHrPKIkM3UEVEEmqXlvSYteRA0bwjtt+ybXTyfBaA069RTzBFFY/VnpPzI+HoWbrRfrPRRr0kTIChShpZe69MIyOJposAsoiRLLWmpnXQNUMCUAScWcMNte83Y4o+3ZsvXlDiXIQCbAWG/P+oV43VZc4eCBKnfaJs1+a3d+s4pI5s9765k+NLfJfnpeeyjay1iDJXO7Qh1gKhhGuc9RJQiQiIykUqOMnt8eG3w8kRAYKRfIosNy8XFr8XOSHk1YcLTaNaSJywiY2zx4JUfDeyNgCI+Npn+xDl44MCB4aV0GmWELU2bnee0bIxDsy7Li5QvxXNEwB2tX+Iv8neknRFgqs3JjBVPAXPmtJ0HSGh6aU1I9UprknrFPENM45OGNEj9WqcfI3KHtteKNeIeZwqirDGqI3c4j6Xe2iaoA0QN0ziO3UuUsRCilnBbfGTz1FkApXkzwDNTX0aBl7SFCvlsm6OACHmxhDDvJ806lfoTf6OX0ll8WEqlNK1F9KQOb0PECqftjBzr5ySNU6lRoRFVjtqWLKaLrBMLINJv2vyV6okAc+RFKpPWi33Kn3VBAIweKmur3iLuIZLaUirjomve669+f/OpuAgortOGLHBvEwwBdICocdoKQFQ6ESXrownL0vvmpfXiKqJ1aenqgEfv97p3J9Vd8E2Ax2ysT2bMeUyY5V1ZW1sr9uK0mZa+ocW/ZxVIibdB4t0Las3GXSAv+//1csDIto3GL8ADhz7oo6m8LupNkbw5EnDWrufwAAjA6M3S0ff3vJORWvsjfZQluqaisWiDwUB9wFWaJ00CEHoppWeYRIF209QBooZpXIBIEsKZSSJZA1q6iJC2JjYXcFaZnC/Lg6Dl9+J/IouwdKF68UkeZcayDlgs5aFUIGXGkSvIEoBel6y5EF0TFr8aQNTa5nkBOE8Z+WDNo42N/KO4UrkYkM1Pskr1W4HdGo+8L+kWlUb0JGv0skatX3m6yDqRxjQzv2kd2v9b7Y6ces2SxT+OyeTkpBk7xdvGy++CqncQjQsQWYsxQtGJ5Qlpj4fsoud8lSh9LV7Fyx9tjwWUSi/Li7SNUwlYjAi/JkBVJJ8HFrJgpJQPiZoCZRpQyQTrYz5ru8NSyB6/UcCU5dkCaZl8UaL5IjFc1pqN8CrNU5Q5kTKjxmJknWj/77W7rg7gZI35+vo6TE5OmrdjYx4eSG/V0SR1gKhh2goP0VZRVmhsJ75K822Hfrd4sYBuRmhadTSVPgI0IgA5U7cE0KVyPEURBUn8dy1WxSKqwCLBs7zeiIIs3T5vAjzXAZxefVFw4Bk+0YsoLdAobbdFrmvIAEsL5PF03u3S2aPyEm+Ud772NF77fXv3ok0Z3AGihmmrgqq3grYTOGiCxt2eNuqTrE6JokI2AiCscjNptbzRMrmVrikMr38i9ZV6rjKB61JdJXOmpD118mWpLYs/OtaRIO7IFpyUj1LpVpUFMqT2RLbu+/0HjvRLpzgRlNA32krklAXMKa8cKFkAva4X3qIOEDVMRxIgatN1uRVU2p5SYNNG/0UBTETIomDS7oiJlJtJq+WNbn9ELNoS70+kjKw3JBN8X+LZy7RHS9NEX3mEMUqZ9+iiFPUGRgyIuteRYF10HEtjYbIeIm1s6WlJXtZgcP+21czMTCjuK8Kv5g2Lej5pzFe3ZbZDaDsfu2+6rK30ENVRvNrv4wY2bXmISsq0XPWRE1NtUtZTYfXBdvFoeGXU9ZZJ1JZ3qQ5FPS8ltN3kmGSERABbXRlVauwgf5KHKDMvNK9VFuiXbDdnqQNEDdNWAaImBddO8PyULMgmlQulrQSGbdJ2aVeph6NOuq0uIwriM1Qy39ueA014Xiht1ZzNKHTcLqUn7zSjg49ZU2NogR2eT7qsMhOMjZ6h9fX1kbHOtmUcY9sBoobpSPIQbSW14SHqaHyUtXybcNGXlmfljZQTVfqRrRC+7ZFpQ6SMDPiMxnI0sVajtFXGXMRDyQFRv//AsyArKyuhaz6aAv6DQfxhYMmzlYnp08DcdpS/HSBqmMbxdEdT9zBkBXtTdbVVRhvfs0oqUk+WIjy0GWgY4SOb1xOW0ptS0SsDaJCmZtl6J2e0dYbl4/F3+qSB1i98W0gr22oj3S5o4vSPVmdkG0fbAtFIaxftBzwCbm3hRMCBtB7GsU5pWdhe65kabDu2l6YtkfER0FIKrry66G9Wv5fI0tJ0pdQBooZpHI+7Ri5UjJYVvcyrLjVRvldGG9+tPNq3SFszC9s6mSQppyY8B1KaiEWpATTNOpQu1QPYfLNx1MtCn1bQjvPiyRqrzdo6Q8CAtyOvrq66/SKBO+Qjej8Xvl/W6/XCbeDtiZymioyzNsZZ4EH7eD+5EVsK8sX00e0j+k1bPxpw0SirtCXAyr9JdWvzw6qPj5nEa3QOlJAnhzzDLXr6sm091QGihqnzEPl1tVXGTvIQZRa2JSwkQZMVGpH0Ec8BLcsKoIzUzW82jrbJ6yv6bIFFloco6oHwys6c3qMeIt6GJgFttDyJsvOO9vH6+jpMT0+LT4RkARj/ps0JC7hE2peRJ/j/KysramAw8orvx2nzg9dLjYCSOaCNmwdi+PjtN57lwP+ndx/R/MvLy+a6pH3T1i3VAB0gapzGEUPUNnjpqH3KjGEbHp9M+gwI94Ropu4sYI/wmQW9JXxH81Pl5wW2rq2tDd9P43VHgYjkDSoxIKK/Z8q3AHekfSWGS/S7li4DkBCUoYdP8/bSd+OiRkTmbivJ0yq9MUbr0bZGuYfPepEA61haWhqCt5WVFaiqauRuJm2t077pTpntIBoHIIpa603RkQDA2gYdbZURKa9U4CN5860tQJHlm1r6GTDlKTZNQNc5/o6WMv4Xt+Yk63/Xrl0wMzMzvCSP86JtJ3rg1GuDt9XkeS60fkV+6LaRZP1nwDWtKzO3+Mknqz3ed6s/cctzcXFRbdNgEPNgZoEnJem2bBwHvt2lAXY6LjQGzPJAYt/QLeaFhQXRs0XT0jWMfdPdQ7SDaFweoqj7uwmqI/x3CmXb2ESfNN2vWnnReixFFxF2mXZE8kSVnCRAJQGs1eUpNgkY1AGz/f7oLcHUq6XVfeDAAZidnYWlpaVwnBu18CVA68VlaVsxnucCx0GLI0PFi+kkhaz1u0bc2xSdW7Ozs+o9SNl5bc0JBER4K3QEVNY1ZCSiW684L6TxsrxDmoFk8Y4AC8GTBkZpOum4vwdi61IHiBqmrXjLLGMRSUGs1uTl3yU3qDcxebqs5ZX9LqXhbcryaPVJpHyp/yN9bLWHjwN3eWt1e0JMigegZfCHF+sIcU248f70YoO4hwF/27dv36YHUbPzC137KysrofReW9fX10V+pXXJlQNVXtyCl2JsMI3UD5Jio+tAAsKRuaTF5GieBeQP+8Ubc299ZOSDpVwj75dFt5J5WyKys64ho/GMYxTx+mrj7BkZEk/8Ny+NB7ybMiQpdYCoYdqKGKKMRYSInyJ/aaJqJ9loWjwJJFlXlOgV8RF+636XFqzVJqnMTJ9kypf6n9eXFSbRcYiUCzB6wktKj96NJqxqjXdejgdKpP7XLNksj56nIjpfuTciM+bSmseYCmyzVYbkKZPWCR8PTdbg/EVPA7fyPYBIvVb0RJW27Wn1d0RBlxDtO42P6IlfDp68sjFPxrjw0tXNk8lvgVbtZGmUr7q8W9QBooZpHICIC+jMJKKWpebtoO8LWZ6MqIdocXFxuH8esaqyi00S2lKAn2SVIu+8LZLHyDt1xMukafhpjLW1NdUjJb3vxIUJtWy5Va9ZylK/ekGWvM2DwUBtJ68jMo5Wn9G8fMtBKkviVarf4svzPmh1W+XxsedzSgusjpRrrWNeRqRt0VfdcR6iseOBcUp0ffL7hyyAoPGsAd+6FPF4RT1EyCcHe1rZnIemlX+W9yZ4ktpcst7q8OBRB4gapq0ARFniC5MvDPod4wioFZQFYPSopWcZcYEbqUPaTrDKxjbRIFUumLjQ1nih6TThxvvTCwyk/GneDWlvn9cV8YREvBveFf+U6Nywykarnm/jcKKnU+rE8UTnlaS0Mg+xWuVJaamXIQIGovVKadtQgN52rZRf+jsKZrP8NUFNla8ZJNbjttoayvJkzbusV60UgEqyX2pfE3KrlDpA1DCNe8usZLFSK1BaGLRMVM4zMzPurcGSMqSu8cHgAa+SdlU9V/iWkqABgp61hQoYrVD0TCAfvB6anrfBKldSoLw/rQcKB4MHXqGemZnZtA3gKWk+N+p64/A+IA6KNaJCz6obhSq94FDiA7dwer3eJs+FB66t+iIWuQdeI1a+d+M2VYietyBTL0+PW1NZBUjnd6mCKtlylIBpW4CnaYryq42LJt8zxgavnxt4lmcy0j7thJjVdg0QckAd8Rp1HqIdQuN+y6xEcPA8dCtJmohUIFpKVlKG0kkTy+NAFT51pVuCFsGF58nBd4Oi8Q50sVpKQbK+MsKK8xnxTEWpriVljZcluDxlzQUhT4/lYN9LADoDDHAeT0xMhL1d/Kh0ZL5raSgA0gBS1COFY9Lr9dStS9puBNi9Xi+1LUfr8tarFIiP372j5JwHvmWvrb0I4G+aIsZodP1rfasBSEvWe2CJG3gZL7JE0s4B5ZEaKVafYXq6i8DbY/VJ09QBooZp3IAoajVoeShp+bOWSdRTE/E2RE5C8C0zLW028LLUSq1jvdTNm9168CwvS0lrgtdS6pqHkOfTAJIFXD2gm50vXEhbfa2tC89DJPHpCX/qWfCed6Dbr7y8yJrQ1jT/HZUs38r1AJVE6BXcv3+/KQP6/fJtn9J1RsdPm/+eZ9STKdr4Rw0QrVxpbZb2g9ZOiR8Kkvh8w/T8pmretziPvdu461IHiBqmrXrtHqC+G9FbSNEFlFXCkboieTLBepF2RILGs30eTV8CwFCQRuO9JIUoWXj0dwn8eCCJEio7esGgBLg1C1MiScl4oNxL0+/LHkWtnVEjIzKuEWsYvSjz8/MmoJG2vClvFGxE1yVtF98m1raLpb7W6tMOCUg88cDyKEXns9UPEijw5n9GpkS2xbVxyIAdD9xkZJBkWNE5EPU2S3/z0IQ2gFEHiBqmrQREbZG3yMdRV4QHSVBo6SPlNZWmJH2mXCoUMwJasr49j0BdICcpBM9q9vrCA1QlvEpgMdNOS6llDQarT6Rj75QwBgzfhLPqkeaDRp7SjqTXxjVbdmbMtbbTcrx+osTBq2eIRbd+rPlnrRf6LbpuuCEVMRg4ZeSz5aHNGr5NUweIGqbDERBlLMe26mraE5O1BuukKUlfap1FLDVL0Was05K2er9F8oxrPmp9oVnUNI/nfeKemcg2CwW86+vrQw+RdfGkBXJ4vYOBvU0o9Q+PL/T6ngKCwWD02gXed8iL93aVtA2bnaeWp8kCXLw9Up00f6/Xc6+PwD6wxi2ybr0+wLmxsLAw0vamAKaXVlojnnGUrStLHSBqmHYyICqZaBnrvSmqU2abi6nNsqPkeYSaOIaf9dZoAq+k/GiaJonX1+/7F5dagdYcSFjlYXpuydMYIn5yLqJUeDtQ+WEsBwchUjkcbEngi+fjsSBSvBAGi+NVC3jKT1PQkgKXQKfWD16cFR1L7jWlY+zF/uB3/kAp5Q3TRPpf6oPMmuDzkPMbMQSzBz8yxs+41zlAB4gap7YBUelElMrJ7n+XeBR4mZnFFl38GSBiWcyl/Gh8ZcppCkxZ4xHxtHh8SF4Fmt5Sxp4nJNoP4waeXIhb3oSoQJfK9OJgaDr+/EXJuErKHkEFV/5SW6Tx1WLS6FqlaaSnQjhI0wLtvbZx0GldC6ABIiovpBOzPPDX8hZp4IoCOs1DF5Et0XiqkrnHCfnhQfTRfNExHOc6B+gAUePUNiAqnYhSOZkAXFq3VGd0ojfhASgpk+atc9rMK7vp/iulJvo5ki8DTrfC4muasnNt3AJdo+yaioDnknK8siwFne1Pzxsh1SmBS+vYv9RW66oAi7dM0LFEFNhxg88C6tRrFzW0rdvsLdLasV1kQweIGqZxeYjW1tZgdnYW1tbWioSuJXi0kxClgixahle/V2Y2wNMTlBmeM3xq/Vcq7CN/Z/jx8mb7r7SNJVSquLXf6yp4+t2an03OjWzdXt6mxms7zZWMYo6eeqNlWF5oT77yI+gSSWXQE4UcjNF28TZgPtyKtQwcCt4ko8iTDaUyZ1zUAaKGaVwxRLj3PjMzU3xhlTYJ6b5+Jn9T1nPG62DlK+G5VNlHKQocPeJ8S0K4lP/tYq15JLUvyruWTurXiKfNUoBYhuXVteqpMx511mR0PUXns1ZenXEspVJePL68/hgMBpse+eUUObkmyWgL/EaNWX59grZ1SuO1aNlWXw0G7TzC2yR1gKhhGhcgkt54yipAaXLTsj0PTSlwkfJSKgValjDilg2PT5Dq0vrH49drN71QLxL/4dWJ7ea3eltWoVVXU6CtbaoDArU2cqUSfVMvckS/roeIxg1FxyUD8LX1bPFNvQuR9SmVI8XeNHFLewkvEkkeIo0/LSgaqd+//36r6elp9f0yPmbS3FpfX4fZ2VlYX18v7IHN9UlXInDZ5/WbNd94QH0Jj3WNPY86QNQwbcVN1dopCC09F65WPg9k8G9RAZxxQ1uK27LmJMsF722x3jCTgIb1CKkE2DQAgoGPCF5Q2NGTOllBhMCAP+WgWW7498rKiupdjHoHokKpDQFW1+K02iiBLY+XNhU48lTnZuZMXAuvV+sLSclllRcFRB6I18prE0hJ80SbO9gW7f077y1HqW6p77PzM9I+6sFsYz5rp/AyPErGXpPUAaKGaSuO3ePiiFyPLy1YT9lar3FrE3V1dfM17bxcyw0dWZCeh4ALV+pF0U7TSGVYj4JqgNESohSI8gBMrDNyyofyQE/nWF4K2i/WG1Ol/W+lbSIwPZI+WobnlYmArUhdTSkXba41MVbRvvCAutRv3thrxpGWz1pbmcMmpfMkYiRadxpRD5G3vWXFG+EbdRmPi2SwZryhVnnezkKmPq/P2zJAOkDUMI37tXsA+zZange9At5lZzQffxneAi5Rr4q1MC1hyMuIbFVg/R6AQpKsVG0Ra3xaitrbttP61Rov+iBmVMA3cXWDpii1OiPAKfv2lVRGE96tKNBo6zRflJpor1eGBtSlcrLbmJk1JP1OlWVmTpeOi5fPMvBQVuP9UVJ/0vxW3+CL89wIsvotehWBVY5Wnhd7yueQFcBtgV5P1tShDhA1TOMARNz1aFl/mhLOoGxvX7wUeHCi+aLWQ2TRZttrWYAcLJUcOfXuVcmSBwx5/XUEiQbUeFtKQRe2RbsXJtKeCJjMKmGtbnzl3bKKrW1l/ndmjLJ9bJVt8UDr4W3hXgGv7yWeo3xpv0sGTBv9p/HE/7aep1ldXYXFxcVhHOHi4qJqDG1sbMDi4iLMzMzA05/+9BGDhxsO3JiU5CiXy5788vqVlre+vg69Xg/m5+fVtUD7QPKi8e+cr+j7fXXosAFEF1100VCI4r+HP/zhw+/f+9734HnPex6ceOKJcMwxx8Azn/lMuOWWW0bK+NKXvgTnnnsu7N69G/bu3QsveclL4O67707xsRWAyBICFqqOWkje3i8vJ6OAKEUVe8aya9oK9KyYTJlNABQsN7J1VdoXkTIkUJzdwqBlWdt5Hi+RdFrfZ8v02mdZuvgb/bvNuV3aX1ZbPa9ApqwMz5qyLpnjlozUyKrTmm8oS1FH7du3z+QL001OTg7/XwIovD9obCL+LnmRrf6S2qgZ1N48ABgFil4faWNLD6R0HiKDLrroInjEIx4BN9988/Dfv/zLvwy/X3jhhXDqqafCZZddBocOHYLl5WV47GMfO/x+zz33wOLiIpx99tlw5ZVXwsGDB2F+fh5e+cpXpvjYii2z0rRZazjihanLc5NlZdNG8pVa85i3jdNbUZ6aGINsGaXbcuOeU6Vleu2TxsY6WdgEr9nfvXKttkbjRiJlSfy11UYpT8ZjqwEyjweUAYuLi9Dr9cwt7sHggXCFtbU1c0s80s8IKKRbwKOyQwMrkZPJVp9JfcQP/NDf2zqFeFgBojPOOEP8duutt8LRRx8N73znO4e/feYzn4GqquCKK64AAICDBw/CxMTEiNfozW9+M+zZswfuuusutd4777wTbrvttuG/m266ace+ZRahUoBRp5ym6myCSizQJvOPo/y2eTxSaVz9WqeepkFmBlxzviN5m1KMWWOlifb3+/Z7a03LPdpG6fLHyLyhPGXnGe9jC+B4Xru21tJhBYhmZ2dh37598JCHPAT6/T586UtfAgCAyy67DKqqgm9961sjeU477TR4wxveAAAAr371qzcBqhtuuAGqqoJPfOITZr18q267AqKMENEWI1/E0TqzE9paeHWswHECOSlfnb5vkz+tjK0Co9l6t4LPrFLm6ZvwnmW8KhJxyz66vpeXl81X25Gs8rjHgAMS6zt+swJzPaJpo0fCMwCPt4GmwXgseviEx3murq6qMZXZceffpPaWllkayI5XNmg3ZFsAta31ftgAooMHD8Jf/MVfwNVXXw2XXnoprKyswGmnnQa33347vO1tb4OpqalNeR796EfDy172MgAAWFtbg3POOWfk+3e+8x2oqgoOHjyo1rsVHqLSyaAJESstd28OBvmTXSXBtpZ7tcQ60PKMCyhleG6b1wzVtcRKrd5svdSi9Cz96Bz0lAGP04jGoCBI0K5y0AiVBL2AM2vVc+KxH4NBLH6LHvm26rDkhWb0SOPT74/ewaSNYck6wz6NHGHXgBNvpwZ+EfRgH+O9aNJVIPTqEj4mUjtxXkknNLnHxQPlyH9kG9Hqc2v9I6ju9XpiwD7neRxy77ABRJy+9a1vwZ49e+CP//iPWwVEnMYRQ2RNfItKvRQaqJHye2mjAstTRtn7WOhjhE3zQ8vRFm+p5SrVQXn1LKlMXSXeBi8/75donEYW/FEBbt2bNRgMhi+Ka5fncd75942NDZiZmRkKczwtI7VN69P9+/ePXEsRaS9a1DSo1AIi+Lvl/dDexPLup+Kn6zJKUfoueX9oWevr6zA9PQ1LS0vhdvO/tW/oicGrSyzvmHaghXpzpDbwdUAvRY3wzC9RteaVdB+ZBnA4Xziu2sW1Uj9b42udUJZOS0u6QzvB2gZAOmwBEQDAox71KHjFK17R6pYZp3EAInqXhXc0OUIZpZcty/u7lCfNQtKEsmaVRwWrZRlKFmBdwGfxZFnOFlnHgfv93AmnaH7kEWMWovdfee220lr3ZvX7D5zcWVxcNANBpf4aDAYja6/X65lP52h9GgEBnBAQabdCa0ZMdDtI4ouT1Z5SQwDXJoIJ6Sg/f/+L8mHJGG1+0jT4DMba2prr/eZ9g2PS6/XCVytE+qU0vfe8i1Qu9TpiPy8sLKTlr0Q49yTjQ2ujtz3I50uTdNgCojvuuANOOOEE+N3f/d1hUPVf/uVfDr9/9rOfFYOqv/a1rw3TvOUtb4E9e/bAnXfeGa53nB4i6WHXEsARmdyloKhEyQKUeVw8S4tb5RZxJaIpFb44o4rBA2/RgEIKALy356SjsVFLL9IuS6ChV0YDDhrhuGW2pmj/ceWA/YV3plhbVnwcUFHv2rVreNPw0tKSGUujeSY9PrW+sACXpvA9kCO1mZYRPYnklYdtkAJrpVgazNPv33+78+zsrHjfkeVp0OYnBT50XVh9Ja1ZGgeTlXFWX0aVPpYRNcSkumld3r1aHu+aDF5fX3cvjo22hcqEbJ97dNgAohe/+MXwoQ99CL74xS/CP/zDP8DZZ58N8/Pz8PWvfx0A7j92f9ppp8EHPvABOHToEKysrMDKysowPx67P+ecc+Cqq66CSy+9FPbu3butj91LFnMUgFgWlpfeo2zZEpUudIvnDC+aa1wSAJHFmeHFa3sGXFGKPtob4T+Tlgv3bFk8sDQiMC1A3u/H7jPh44CAmipmz/uC9VNAqJXvjbPXVk/BZj2X2PfZWCdeHveEUY8mBYU8lkYzbHgbo2uV9oXlkdL6SjPCstv3lA9rmzEjV7Tt4ch8KJXRkTbROe55sq220DatrupPo9SlwwYQPetZz4J9+/bB1NQUnHLKKfCsZz0LPv/5zw+/48WMJ5xwAszOzsIznvEMuPnmm0fKuPHGG+HJT34y7N69G+bn5+HFL37xtryY0aLo5LZAVaZcTUhkFqdVZ1PxMW3ljfKoWU4Rj0vUCvMUe2TsvDoy8Wda4GbpXKLWJt9SibSD8mV51TT+UXGiJ8Mrh5ZFjznzt7s0Aa8prcy4Wf1gKRzkUws2zqwPHmzMPUTS+GhlSwA0K2/aWC8anzS+jM7/iFLPAmL+uwTCpXxZeSKV4z1txL2gGXBJ64kAqzp02ACi7ULjBESeAIksfMkKyZTLrS2tzkigJs/Dy862LbPgImVKabKWFipD7V04a1uPCgXtxIjUPm55SZ4WzjtuBczMzGyKOZLGhCtSy/NB+4FuJfG+5AKUbp1xjxPnyYuXovOR14XpcItOG6PZ2VkzgFXrHwQZCwsLm7YDtXKwjQsLCyOnzLR0mudLW/NIfAvI2iq2+MX/X1lZMU9vDQabt3wtIGgpeakdkYdPI0BMazMtg84hGjvD50i/39yNy5pM6/f7aoypxqu05egRzjfr8Wupb3mduF6XlpbM+RbdXi6lDhA1TOPaMlteXoaZmZmh8pSEoDexudCgCtea5FxgUSuSK0EsE49YSgLKUrLSEWAq1DX3OvKBx1pxQaIgkmJZ+v3RWAVJsWkAEE/BYDyJJlh7vR5UVQXHHHPMJi8HxjKgAJXGE5UiKhHkF3+XhCCdL7t27RqmR8FCy8KTizhePMCSgzYKfijfnusb24q80LJorA7dWqHp6Pivrq6OBDfT8veTeCn6dAE9tYPzgh7pxj6gVjwV7tjXPJiWrw9pnvd6vWHQt/d2H60T28SBKq0PD1zQh541wCTViTJhampq+Bp7dEuI8oH/j/Odx1jRdcpPzknglM8BjxdargWKOND2tmKt+CJ6upEaBzQPzp1TTjkFJiYmxAe5M8aeNHct44jzSseA64EI4EAZjfm1eUxlK8YTSet1enpaPHyR4akOdYCoYRpXUDUudlTelqdHm0SaZbC8vDy0fiPeGao8uRKkyF6zTC2rVbJQqWDkAIx7QxCsYTvoQuSKazAYPc2iWcGSF4cqK+s4NyqIiYmJkfZim/DNIvx/qW7qJaFHhdEalwAf8jc5OTkCfLB/URghIKNAiXuzaD9iW6kSoFta2hzEObu2trZJgVPgp7nYOfDlAlbzEKEiQKC8vr4ubl9IXiI6H+iWiOQN0sANneeSR0VSJNT4oC+cc48flsHjnCSPiyQzUOFzr0YUiEjt0Qwhuk4xyH1lZUX1fkb7iPKGa806icsVtad0NbBLwRIalJLspEYP/pc/iCqNlwY8sd7FxcXhWuXjzwlB2dLS0hC0STJNy8vXFZXRWv9sbGwMZRvKGQrAUNbgGFje6zbBUQeIGqZxHbvnLwtnUT0XNvT3iJubu1y148CSQJOsCEuJ8K0Yav3w37hi41sznrKmXgQrBoa77umY4PFdSSChBb9r1y5R4SN/knCmfSGl431FFR7tB+pRwf5CpWTFi9B+4ICAe3ewXm3LBcul1qWlWDXLk7fLcqVLSosCSm+ucr6ol9byhnEepHRaP0nKgLZBA+zoCaTznI8f73dqENETQZLBIhkGXC5gOmmbi69LHA/q/ZSAo9WnfI5wQ02aC/iafHQMI8rZMvDolSn0wVbqyaRlamCP14lAa2FhYdMVBVL9OG4UdGg8U5I8r1Fgj0ANt8VQdvA7lrw+0EBXE9QBooapbUAUnbgWeYtMEpY8v+Qe1qwYKX/mYkksy/LuUJ4k6w2Ft3diRhL0UlskDwH+5l34Z8URcV6ocKF8UAtbE0BabBf2FVUUmheA8mFZbcgP9WDRPpEAkcQHbzctV1P+EfBF+x1BAp8TXEF4ig+Vw+zs7CbPhmfN8t+zvFsgcTAYbLpPhnsBNCWEHiLP24zzha4nbQ3SG4ml+UI9yLt27YLJyckR73RUAfJ0lhyjW2redr8FxDRZbM0fOn7RoHxPRtL5YV31QOcs31azwDc3MLMnVaX2a95dL69n+NShDhA1TG0DIktxRUma+NyVb4Euz0qV6uDfUPhFTgtgWd7trtaeefQmWrRkcGtJs1AlkGBto/A6IkdluYC3hIrX/1Thapa8xrMHvmj+6Iky/OaBaUv587XggQpu3fKy+daTZ5VKIEXbwuXzyBpbj3etTEocdON6pvFjESCtkTQu2ngi+KBrXcuPQG52djY0hySeIvmQp6mpKVGmeDIw01eYNurVsORNRuZbc1bampLyo8ESNV4jBoC17rwyM/1YQh0gapjG4SHKKDOvLFwUXsyGljd7bN87ERUtI5MX81mXjtGyuYcoUrbXZ9F0EWWnlWvVJXkEosStwroC20sbtUIzCjBTrpS2CaVMxzZraUvgC29YlgJzOT+S8mlK8VqE8ip6b0y2X+rKPisI2AM7Tc55Xrd0d1YWCDRh5EgxZVne+e/ZvuUysfMQ7SAaV1B1E3uqfFFYwYBImiUspZGsfuoij4AHje/ocWepvVIeazuoCYqOURN1c+Hj9bfHi+X1kOrM8FfyfRxUhwfLSvY8q1Z+TnT7o3Qdtd3P1pprYn211YY2ZEC0/KjRW8Jj1pDlAdgSkI4Ye9yY9bZlLcOw3998cKAp6gBRwzTOm6pLPETW5B0M9GPufKJaCpYLKbrA6TFty1WbPf4qHTf2+o3WVQIYNGrD8i4dYyuewKtD2wKUeJEse084llreTSir0joia8+av2gdY/AwXRNU4EdimdBDtL6+LvJorTGaLuvlLVHAVv+U8uHFwNThsU76yNzJGAVaeSVgMFIu/V0Koub1ZmQopqXXoWR4Q/6swPE61AGihmkrAFEmjTfprNMnmDZymocKOO00TNbKsNqFC3d6elrlLQJUmhD6EUCXVUBRYcHzYt9LAcOWIgbwA30pSW1uAmxmAUe0jEzbKGnKwJrbnBd+ZQEPGLcUJ/1b64dsv0f7s0QBSyStl8wTIcgH9zaXenmz7cqAGel33EKMBlNr5dWVVRowpdvrEUMnCsC9tBHdglQa3O1RB4gapq3YMsuk8RYaWgXWi/BZAZqxHEqFcuTkVp1+y/DkCaqStmasS5oX+14KGLYUcaQdSJogywhKXp43RyPgmfYF3wouBUQcTEa9GpTw1NXi4qJbhjQm3NtqWfiZNrXhIYrUjVso2bg9bnBROZMBEVmPTx0PkRSTU6e8OoYG964hUOeXnEbqy4AZq6ymQHcpdYCoYdruHqJImtIFWpquyTK9RdtEv9XNU7etddvBFUpdBecJsWz/aECwpG6s3wP5WaojuEvBWBN1bzfKGE0aWaCxCYXb5PxGcE4PsJSOZ515wPP2+w+cKNNOyVr11Z3TSCWAtUnqAFHDNG5ANM7JstOprb6qa6nt9PHLArSmADdA+Ym0urSVYz7uuq2xa7otTYyTpbibbj+tjwbMawpdyh+Nv2uqr+jlsxxEWqcBLQMt8sZc6TbXOA2ADhA1TOMARBSNbwdrcaco9bb6qklL7XAl2s4m29xkWW14NNvKX0p1PSSSZyEaNxL1oNQ9lZbxwEa3ebBMKx4S5TI//er1EXqN6HahlEcrJ9s//HJGmrff909w8fowz/T09AjIsur1eJWumRiH16gDRA3TuAHRdgAjO0Wpdx6iraOoh6gt5RfJG53H0hZclrZqzZR487STXFFAISn8DH9N95UGYKy+oV4gy1sm9YflGcE8NG5HK0f7PesR057vwPT0ckztQlSeJ3LR7vr6+siFtxZJp9sotbV+OkDUMG2HGKI6SiWLxj2hiHkzgaOZ3+pSnb4sdWE3ma4pAJH9XfpuCfJIGfwdtEgbSsEVBzUZb4Gk4DPjgHVFLyvMli+lz2xXcA+0BmYiQAfbKt07E6Um1yjlm159gOk07490oaUUzC2RBKaQsK8pIOLprRvUvfZrwMG6jsMCTDQvvbR0dfWBB2a1OSadYNbGSrtSItLmOtQBooZpHIBII75Qo8KHLpqMqxa/aW9m0bzSwrLK16yQ6IOPtE8sRedZV5aC5nmjVoslIKO8ZdJE2qMFRXrl87kT3TKQQBD9r+dxiGy/ZeZtth+poooAOTpfI2si0xYrPc6xffv2he9t4R5orW3e+pD4iIDirKKLzgEPeEuywpJN2Kbl5eXQW1ySXJbu+pKeXSm5ToDWzYF79I4q6XZ/3id4anJqasp87gnjjCJB2xS0Ro2GJqgDRA3TOAGRBj4ky8LKH7nOXwMVVCiurq6qp3k8DxEvX2ob3aeOgj5U0trpCcvLw5Wm1f9RDwNN57UhYtV7aaLt0ay3Oh4ia+wpKECerKdVrDqteRX9VlKO1IaoNyqr/EsVIc4x6SI8rfwm+ibLdwlIjNQhAVELWHBlr8lD+ls0hCEi5ygPUTmeIQmAWSQBWs4zgm0E3JrhiQBHu6Way47INlzT1AGihmmcgMhbvJ5VmbE6rbRU+JY+POvxwhdI9JjuYLD5IjyrvRGhWcJ/SfmRMjNj7AnrunExGd4kUJCZj1JZbVmRGl9RYOMB6zYpC0iy876k7yVgIG3hlI6rli8CnKJvvmnlRtYjzgXpIWS+lSr9ZvWlxysaPr1eTyyPb1VxYC3ND+nUmtZHqCP4u5mSIZ3dVm6COkDUMG2lh6jp79m0dRRTtPzM5V+87Iil1bQQrlN+E33etnJrsrzMOFFqG3DU7Ze2AVsTlFHqlEpAbL+/eWtVKqcUIJfypG1/Z8qLrEfqUZTuAuL9gh5uyVPC89Q1LGlAtdYuqY6s/KMAS1q/pWNflzpA1DCN47X77S5cdxI11Z87dVwiVnMJ+KRWbxaQ8a1EDyg14d0qAc0lWxm0P7JKJLoF3vRctsqNvpVHy6AeBe3G76zng9eFeaM3ilttrjNm1rry2kx/i3qIIlvoq6urcODAAZiZmYGlpaWRteYFMwPI2/SeF5XzXfdYfVvytgNEDVPbgKipG0F3IjWhoDnVsXa8ckp5GidRvjlfTVnaUe8N5uVWK855a8uz1HuIeTWrXeMz8/aW1MaM5wHT8eDaSACw1t6Issk8xIy80fg4Wr7Wbs6zBoqttknt4XxZ45pZj9kxszw10TozDzRr/cnnCHqCFhYWVCNDq0tqm7ZtFjmsoYG4LA9NUAeIGqYOELVHTShoTtKiK+njrFCts8XTJKCiwkgSplmQIVnaUe+N1q5oIGipkKTjEfUQ7d+/f3gsuTQ4uo6HCGNB6DZKtDyvnySQE/V24Fjx7Z2ox0UDxVnlyD0r1hhF5k3WK1hiiGmEQcszMzOpmDxp7WG/eEfkswBUmo/4O5elXK7sZ3cOaaBYa2eT1AGihqnbMmuP2vAQSdQ26MyABImatI48MKDVFenXpoRXE9sUVrrI9oZlQZfOlzpr2bvTxarTA7m4bbK2tla03qwTqR4YjHhDvPGzFKrGc9bzUpcyxgZ/DDgK6jTDC+fr4uKiur2WnZuZU6qcV+4h0kCx1s4mqQNEDdPhBoi2CoC1VW9GkbfZ5jp1NMmfB840UBPxvDV9aq0pynjovLSDQewdp4hi8MY16l3KelM4cYs9U75FtG5N6XHvlMSnpTAj30uoDZlA+bTAUanM0uYLAqKZmZnwUxoeRefuxsaGC5gtA6r0PbQodYCoYWobELWNkLe6vrbr3ar2bGcqsZAjWwJbddTcowxY89JG26lt62RAZnTuZrc7OHlKp3QNSQo6G0BP02geoK32omc9ldo2Y7auKBDG8V1bWxveSF1HHmbkRwToWrx0HqIdRuPwEJUGj5bUtby8bO41a/miAiHiXq9TRyRfpqytELal3oBsWVa6bB9Z80Yrq2TMIwpUyhuxNLWyaVmRl8r5PNf61ruYMnLfS4Q/j9pae02XFQUDbfPBKau0cX6U3Lkjed08IEzTlbQ7A+S5Z0iLXYrKjOjlraXUAaKGaRz3EI3Ly9Hvy08xRPJFLdlSy6jJPsiUtRUeJqvOLD9NeBkidVjjqpVd4iGJWJ5S3jrWqKSE6PaMVxePOYqcosNy8E6a6JMGuP2lnSaKtrsUxGbS8KPyJcCA3u6cMRy9+dCEkeelyd4izfssarzWNQp5X0U9U5ZciMqMyCnQOtQBooZpOzzu2mQ9dS5CbEJ4lNTRlteEp216HLTypMcUS3mI9ltdyz0aExGp0/ICZT1EaGFGjjBHQAD+v3WTL/9bA0SeIsR+xZiliCHB38WKKBKp3aUgNpJmMBi9LLBE6fFxoOWVtjnbBj4PNNKUvwSMmwAbEcoYQHXkpbUjoAFi6kFr++bqDhA1TFv5uGtH99O4vDj9frNxMlGl01b7xtVvlCIntJrkywsYLqVSJZHNi+nrXoeQTVfXQ6RtGSIYmpiYGHq9sh4iWh49pVbH48TL9YC75KGyDA/tAkbOrzX3qTGQ2XKLbPNmKToPIkYLfyx3XN4hgA4QNU7b6emOuukz+aKTvVQgN1FX0+1HYd7USSpN0VkKtK4g4/WPw/NISQNETbeRe4jqxiBYPHmAx2tPJu5HKivbdzRNRAGXrE1aLh1zPK49MzMTHhMOgGhgsMS/BbopqOBjxIEaB3DUEOLttvoxetOzB3wzXilKbRgFdT2FVJbyU5uZiynrUgeIGqateNw1ery0xKMRVfp8skcFREaQZPiRqPS+GKufPYWUpewYef2epXHmtwR+RIllwHWTXiZtDmoKis8fT4FFFJZmTdP6NIDAiaax+jaSLrLOJUCUuWCS1oF9NT09rXqDLNCN48i9D7gO8Wg6fStMuqU8AnpRsc/Pz28aXym/J+ey8p/z0STAiKxJC+hTuUfjvzDPuK7v6ABRw9QmINIWXfT4qbXILAFHFXTE+yLVo1mtkkK3rCLkJxpUSmllZQWqqoKVlZVwHsp71BKro3yzgC8LKD0aZ34rrQWIoiAwquizpIFWTUHx+eMpsIiHCOuS7kDKeois+qLrVkqvEV3jXPlF1hnNjx4/K0YqItuo55CWj5ci4oWDmkcpMu8RvPV6PTUmEP+WvFDRdkXJ4tkru6RuBL/SZaKaHKOANTqf61AHiBqmNgERVRKaoPKEVl1QFVn41sTnZUgTXGtnCb+UvBtfNWFHv0diNywesgoj6gUpfcSytH1auqhyldJG5rRUtzbPI9uPUh9m+5zPTavvraDSyLzgfcKfatDaZs1FycuEpIG/UsVkrd+MxyNqqEV44V6IEiBdB3jy+UvbZh0kiLZVm6/WQ66enC/pIzRIq6pSy+XeqxLgWYc6QNQwjQMQLS8vq14Eb6JyoYrlaEIIhbj2JlAE0HAL0AMcvV5vmD+i7CQQKLUH+aLlS32HfSJ5oTg/0XgPqW1WWjq+lnKnvFrlUkWkCdvolqK1zWFZ91yJSYpAykv5ioIfKR2PN6F8SUGb3vopUQo0DxX+JdvSnCfJA2UpEPxm3bSNfYanv7JAWzOotO3njCHA10gpcGkCXEXqiabV5hjlN+PZ4fMb+x7fSJOuZIiA+8gco+QZygDt3ZIepQ4QNUzj2DJDpSYpbG/CUIHkuWVR6ODdJ9r9ERMTEzA7Oyta+v1+f2gVePes4IKRXNSUP+7poUpYU8yDwf0Xf/V6PZibmzMDefECPKnNnB8ew2D1O/ajJxQk4CAdqaXpVlZWTDDDx12Km4gEONNxom2g5VteEKrEoh4iy2PI22eBGRwrfKqAW86SNcq9IxoIiioFmofyE3lcVjMEOBCS1oMkI6R2SwAmEjOjtT8yLlb6aF9GwakGqK0+L1HAHnj1gBeOtXRyrLR9fEtycXERZmdnYWlpqZbXpQ4Y5d+1u5TaBkJIHSBqmMZ5DxG19qOL2fIU4H/pvjwCL7xhlAfiDQYDmJ2dVd/EGQwGMDc3B1VVwSmnnGJ6UyKAaDAYwNTU1BCEUEXA4wAkJe4BEsy3tLQ03O+3PFrLy8vDwEu0bHg6BFgHDhyAXq8H8/PzpmWLv9E7czQPkRSLIVmaFHxowtYDJHhPjncfkBY/xnm0hLen1LVx0xQNFbY4h3kdvE84gMOxlOZwicCmTyjQdRg5iYP847hwoGR5WiSgx+UCPwLP/8tBmLVGIv2SDfSV5AKff5oXis8TC4RaXlNrzmn1SOOnbVVq9wpZ4xvpb343VWSLchxkgdoMUK5DHSBqmMZ97N5ytXvClVtX6DFAgENdqTwNLVPbikBCb8vMzEwoWHLfvn3ifj62CcEHjwWy3K0cEHmeFLqN4Fm/KJwo35SoJyCzpeG5jykPWl9FvnnlouD2PHyYT/OqRLakaJ+WCmmpbVJf8HGQPKHjEMyWIuDpKFihsT/W+ErKmssN2ifoFdY8wpkTc1GKzHWJD4k/nH8eb5ZMQ/JAi7a+tHro2vDAlrVNjnPV2sYEkAF8xMAppazxov3O+9MzeJqiDhA1TOO+mNGaHFlXJvcQSRd3lViCdaw/SZhr1pEX0Ku5oK2+sBagZRVKfFFPQKbfrPgk7iXyrFSPV4mvOn1Cf4vcAVRX8FmKhJYredOs+RHxJmjt9ygTh9bv94fbo/Rm64jHhCprz6NhrTH6nprHe7Q/tMsCLU9IZJwjY+VtU0a+eeksj2xmm5SCYs0AoyQBTb7lzcFzNI5LoojBYKWJyGkLSNalDhA1TIfT0x0dNUtteBfaLNciTyhJglWy+rytHSsAV1oDXl+UPGWhlSvxUDIW2Ty8762bh71t9SyhJw3r98BWtG3afLE8IZl+a8JwrEPUc6W1Q5rntL+5t4/+vwZMpZNkg8Goh5CmoZ50L5ZQokga7z4i7wkSjCFdXl5W6yilDhA1TIfT464dNUttCd4mys2W4QEiSbDxOjzhR5V65KRNxLpFizny2KnnAYiCJI+yXjGeRvICWEo2yov2nQbOW/cxcQVu1YfziSpirAs9Ibwtmb62ZGZWnpaOsRbeQEG6FNslBdLzuum2fASgU+8UhkjgNqMGiDzPTtRzKhlGEe8kUuch2kE0Tg9Rnb1fbQJLvzfheo7wk3HVlqQv4e1w8MZF2tC0UoiMT3QMtTnJFYUVTwdwv+LBoPbIMxGeJVoX/Eh/R4AMb7t0Ws/qV2uNW3E3PF80wNzih24B9Xq94VaOtD2r8RYFkU2dYLK8PRlPFG8X97h5IJzK3eXl5eFhE3pgxGsbAqCJiQlYX183x6p03nC++QGPyJZutI/rUgeIFPr93/992L9/P0xPT8NjHvMY+PjHPx7KtxVPd5R4irS80u9ePfjdCk6M8JN5rbkkfQlvh4M3LtKGnQj8aLuoktKAeTZwF+9p2bdvX2M8c5ClWcuW8veCaqPrVQrut4AYLze6NnCtSl4HeiCDAgJJlmhzNOoxiIx/FFxp4NvrE8lTQvvc8qRo+bGvcL5GZCLmx/kYlaOckAe8G8+K38Q4R9p3GrjcKrnbASKB3vGOd8DU1BS89a1vhU9/+tOwtrYGxx9/PHzta19z844TEFk3jXq0Uz1EtK42PESeBb8TqYnx2U4ktcdTJACbA4I9igY7c76ssjnI0ua9Vhb1qOzbt0+8Ryy6XinoigIB2u+Zm721i0O5QiyZq1FAFBnPqPGQ8aJo5UfHKSJ3MRA5+uI9BWN1gqgjHiLNcOHrlpdbh69S6gCRQI95zGPg+c9//vDve++9F04++WR4/etf7+YdJyCyLJ6MIt8OaaP5NYsiUk5EyEYs5bb7oGlhEHVrtw3+mpoPddqRUUhZniPKVFLKGWvYsrazVDoeddZgFkCMq+wSGVEyh9paY9m4mijfUTnknfAtaf9WeIk6QMTorrvugsnJSXj3u9898vvP/dzPwU//9E9vSn/nnXfCbbfdNvx30003jQ0QedH6UcXuKRhKmYUXtQYydQ0G/nMjWvsibngUAtaR1ohAjFjhlheAbol4QtqzfPlcsG5mblMAeVsAkfyZh32lsiOeJI9nrZ5SD1x0Xkg8Zb1YTdBgMDCf+2iynjrgITN2kgyMAK3InK6zrqJgIiKXs/2JciiynZbp67oguk3qABGjr3zlK1BVFfzjP/7jyO8vfelL4TGPecym9BdddNHIhX/4b1z3EGmUcWVmTqJkABEFL+gurXO8ONM2qX2SwpJ48izgiFKV+pan1fJyUObFZnmxEZqA5uVuhYco6x3JeES8spvyEDUNJDN8b4UVDdDuSR+kum3LjJ125L1EyfN8ddYVLcvip415mvEQbcU6aYM6QMQoC4i20kMUIWuilizUOii/aQuh7nctTYmr3bIitd+i/Nf1EJWWOw5q02ocl4XZdD3bsY1bUe9W11Faf5N815GfbfK1E+uPUAYQ7QIAqA5z+v73v1/Nzs5Wf/mXf1k9/elPH/5+wQUXVLfeemv13ve+18x/++23V3Nzc9Vtt91W7dmzp2VuO+qoo4466qijJiijvyfGxNOW0tTUVPVjP/Zj1WWXXTb87b777qsuu+yyamVlZQs566ijjjrqqKOOtgMdtdUMjIte9KIXVRdccEH1qEc9qnrMYx5T/c7v/E71ne98p3rOc56z1ax11FFHHXXUUUdbTEcMIHrWs55V/cu//Ev1X//rf61uueWW6pGPfGR16aWXVieddNJWs9ZRRx111FFHHW0xHRExRHWpiyHqqKOOOuqoo51HXQxRRx111FFHHXXUUYI6QNRRRx111FFHHR3x1AGijjrqqKOOOuroiKcOEHXUUUcdddRRR0c8dYCoo4466qijjjo64qkDRB111FFHHXXU0RFPHSDqqKOOOuqoo46OeOoAUUcdddRRRx11dMRTB4g66qijjjrqqKMjno6YpzvqEF7mffvtt28xJx111FFHHXXUUZRQb0ce5egAUYDuuOOOqqqq6tRTT91iTjrqqKOOOuqooyzdcccd1dzcnJmme8ssQPfdd1/11a9+tTruuOOqXbt2bTU7W0q33357deqpp1Y33XRT965bi9T18/io6+vxUNfP46Ourx8gAKjuuOOO6uSTT64mJuwooc5DFKCJiYnqwQ9+8Fazsa1oz549R/xCGwd1/Tw+6vp6PNT18/io6+v7yfMMIXVB1R111FFHHXXU0RFPHSDqqKOOOuqoo46OeOoAUUcpmp6eri666KJqenp6q1k5rKnr5/FR19fjoa6fx0ddX5dRF1TdUUcdddRRRx0d8dR5iDrqqKOOOuqooyOeOkDUUUcdddRRRx0d8dQBoo466qijjjrq6IinDhB11FFHHXXUUUdHPHWAqKPq9a9/ffXoRz+6Ou6446oHPehB1dOf/vTquuuuG0lz5513Vs9//vOrXq9XHXvssdW///f/vvra1742kubLX/5y9ZSnPKWanZ2tHvSgB1UvfelLq3vuuWecTdlRdPHFF1e7du2qfuVXfmX4W9fPzdFXvvKV6j/+x/9Y9Xq9avfu3dXS0lJ16NCh4XcAqP7rf/2v1b59+6rdu3dXZ599dnX99dePlPHNb36zOv/886s9e/ZUxx9/fPWf/tN/qr797W+Puynblu69997q1a9+dfWQhzyk2r17d3XgwIHqN37jN0bejer6uYw+/OEPV//u3/276uSTT6527dpVvec97xn53lS/fvKTn6ye8IQnVDMzM9Wpp55a/bf/9t/abtr2JejoiKfV1VW45JJL4JprroGrrroKzj33XDjttNPg29/+9jDNhRdeCKeeeipcdtllcOjQIVheXobHPvaxw+/33HMPLC4uwtlnnw1XXnklHDx4EObn5+GVr3zlVjRp29M//dM/wQ/8wA/Av/k3/wZe+MIXDn/v+rkZ+uY3vwn79++Hn//5n4ePf/zjcMMNN8Df/M3fwOc///lhmosvvhjm5ubgPe95D1x99dXw0z/90/CQhzwEvve97w3TPOlJT4IzzjgDPvaxj8FHPvIReNjDHgbnnXfeVjRpW9JrX/ta6PV68Nd//dfwxS9+Ed75znfCscceC7/7u787TNP1cxkdPHgQXvWqV8G73vUuqKoK3v3ud498b6Jfb7vtNjjppJPg/PPPh2uuuQbe/va3w+7du+Etb3nLuJq5ragDRB1toq9//etQVRVcfvnlAABw6623wtFHHw3vfOc7h2k+85nPQFVVcMUVVwDA/Yt3YmICbrnllmGaN7/5zbBnzx646667xtuAbU533HEHnH766fB3f/d38MQnPnEIiLp+bo5e/vKXw+Mf/3j1+3333QcLCwvwW7/1W8Pfbr31Vpienoa3v/3tAABw7bXXQlVVMBgMhmne//73w65du+ArX/lKe8zvIHrKU54Cz33uc0d+e+Yznwnnn38+AHT93BRxQNRUv77pTW+CE044YUR2vPzlL4eHP/zhLbdoe1K3ZdbRJrrtttuqqqqqE088saqqqvrnf/7n6u67767OPvvsYZof+qEfqk477bTqiiuuqKqqqq644opqaWmpOumkk4ZpVldXq9tvv7369Kc/PUbutz89//nPr57ylKeM9GdVdf3cJP3VX/1V9ahHPar6mZ/5mepBD3pQdeaZZ1b/43/8j+H3L37xi9Utt9wy0tdzc3PVj//4j4/09fHHH1896lGPGqY5++yzq4mJierjH//4+Bqzjemxj31sddlll1Wf+9znqqqqqquvvrr66Ec/Wj35yU+uqqrr57aoqX694oorqp/4iZ+opqamhmlWV1er6667rvrWt741ptZsH+oed+1ohO67777qV37lV6rHPe5x1eLiYlVVVXXLLbdUU1NT1fHHHz+S9qSTTqpuueWWYRqqpPE7fuvofnrHO95RfeITn6gGg8Gmb10/N0c33HBD9eY3v7l60YteVP3ar/1aNRgMql/+5V+upqamqgsuuGDYV1Jf0r5+0IMeNPL9qKOOqk488cSur/+VXvGKV1S333579UM/9EPV5ORkde+991avfe1rq/PPP7+qqqrr55aoqX695ZZbqoc85CGbysBvJ5xwQiv8b1fqAFFHI/T85z+/uuaaa6qPfvSjW83KYUc33XRT9cIXvrD6u7/7u2pmZmar2Tms6b777qse9ahHVa973euqqqqqM888s7rmmmuqP/zDP6wuuOCCLebu8KG/+Iu/qN72trdV//t//+/qEY94RHXVVVdVv/Irv1KdfPLJXT93tOOo2zLraEgveMELqr/+67+uPvjBD1YPfvCDh78vLCxU3//+96tbb711JP3Xvva1amFhYZiGn4bCvzHNkU7//M//XH3961+vfvRHf7Q66qijqqOOOqq6/PLLq9/7vd+rjjrqqOqkk07q+rkh2rdvX/UjP/IjI7/98A//cPXlL3+5qqoH+krqS9rXX//610e+33PPPdU3v/nNrq//lV760pdWr3jFK6qf/dmfrZaWlqpnP/vZ1a/+6q9Wr3/966uq6vq5LWqqXzt5MkodIOqoAoDqBS94QfXud7+7+sAHPrDJhfpjP/Zj1dFHH11ddtllw9+uu+666stf/nK1srJSVVVVraysVJ/61KdGFuDf/d3fVXv27NmkmI5UOuuss6pPfepT1VVXXTX896hHPao6//zzh//f9XMz9LjHPW7T1RGf+9znqv3791dVVVUPechDqoWFhZG+vv3226uPf/zjI3196623Vv/8z/88TPOBD3yguu+++6of//EfH0Mrtj9997vfrSYmRtXI5ORkdd9991VV1fVzW9RUv66srFQf/vCHq7vvvnuY5u/+7u+qhz/84UfcdllVVd2x+44AfumXfgnm5ubgQx/6ENx8883Df9/97neHaS688EI47bTT4AMf+AAcOnQIVlZWYGVlZfgdj4Ofc845cNVVV8Gll14Ke/fu7Y6DO0RPmQF0/dwU/dM//RMcddRR8NrXvhauv/56eNvb3gazs7PwZ3/2Z8M0F198MRx//PHw3ve+Fz75yU/C0572NPHY8plnngkf//jH4aMf/SicfvrpR/xxcEoXXHABnHLKKcNj9+9617tgfn4eXvaylw3TdP1cRnfccQdceeWVcOWVV0JVVfCGN7wBrrzySvjSl74EAM3066233gonnXQSPPvZz4ZrrrkG3vGOd8Ds7Gx37L6jI5eqqhL/XXLJJcM03/ve9+B5z3senHDCCTA7OwvPeMYz4Oabbx4p58Ybb4QnP/nJsHv3bpifn4cXv/jFcPfdd4+5NTuLOCDq+rk5+j//5//A4uIiTE9Pww/90A/BH/3RH418v+++++DVr341nHTSSTA9PQ1nnXUWXHfddSNpvvGNb8B5550Hxx57LOzZswee85znwB133DHOZmxruv322+GFL3whnHbaaTAzMwMPfehD4VWvetXIMe6un8vogx/8oCiXL7jgAgBorl+vvvpqePzjHw/T09NwyimnwMUXXzyuJm472gVArhTtqKOOOuqoo446OgKpiyHqqKOOOuqoo46OeOoAUUcdddRRRx11dMRTB4g66qijjjrqqKMjnjpA1NH/397dhTT1B2Ac/y7tRTyR0mwhhVLOlJg6sYsaREiN3kZdZBB0RsKkYhiLLqIovRGhqy6KCISgoEKILoJevBq93ZSs8qWgsNogyF5GRSoJq/9FOBq+9DL3NzvPBw6c/X6c8xzOxXj47XAmIiJieSpEIiIiYnkqRCIiImJ5KkQiIiJieSpEIiIiYnkqRCLy19q1axdbt26dsnzTNGltbc3Y+R8/fsyiRYsYGBjIWIaI/Bq9qVpEpoTNZptwvrm5mf379/Pt2zfy8vL+n4v6waNHj6itrSUajWIYRsZytm3bRmVlJUePHs1Yhoj8nAqRiEyJ169fJ/fb29tpampK+Yd6wzAyWkR+JhAIkJ2dzenTpzOac/XqVRoaGojFYmRnZ2c0S0TGp5/MRGRKLFy4MLnNmzcPm82WMmYYxqifzNasWUNjYyOhUIj8/HwcDgdtbW0MDAxQX1/P3LlzKSkp4fr16ylZPT09bNiwAcMwcDgcmKbJu3fvxr22RCLBpUuX8Pl8KePFxcW0tLTg9/sxDIOioiKuXLnC27dv2bJlC4ZhUFFRQWdnZ/KYaDSKz+cjPz+f3Nxcli9fzrVr15Lz69atIx6Pc/PmzTTvqIikQ4VIRKaVs2fPYrfbuXfvHo2Njezdu5e6ujpWrVpFJBLB6/VimiaDg4MAfPjwgdraWtxuN52dndy4cYP+/n62b98+bkZXVxcfP36kpqZm1Nzx48fxeDw8ePCATZs2YZomfr+fnTt3EolEWLp0KX6/n5HF92AwyJcvX7h16xbd3d0cO3YsZeVr1qxZVFVVcfv27Um+UyLyO1SIRGRaqays5MiRIzidTg4dOsScOXOw2+00NDTgdDppamri/fv3dHV1AXDy5Encbjetra2UlZXhdrs5c+YM4XCYp0+fjpkRjUbJyspiwYIFo+Y2btzI7t27k1mfPn1ixYoV1NXVUVpaysGDB3ny5An9/f0AxGIxPB4PLpeLJUuWsHnzZlavXp1yzsLCQqLR6CTfKRH5HSpEIjKtVFRUJPezsrKYP38+LpcrOeZwOAB48+YN8P3h6HA4nHwmyTAMysrKAOjr6xszY2hoiNmzZ4/54PeP+SNZE+Xv27ePlpYWPB4Pzc3NyaL2o5ycnOSKlohMDRUiEZlWZs6cmfLZZrOljI2UmK9fvwLw+fNnfD4fDx8+TNmePXs2aqVmhN1uZ3BwkOHh4QnzR7Imyg8EAjx//hzTNOnu7qampoYTJ06knDMej1NQUPBrN0BEMkKFSET+adXV1fT29lJcXExJSUnKlpubO+YxVVVVwPf3BE2GxYsXs2fPHi5fvsyBAwdoa2tLme/p6cHtdk9Kloj8GRUiEfmnBYNB4vE4O3bs4P79+/T19dHR0UF9fT2JRGLMYwoKCqiurubOnTtp54dCITo6Onjx4gWRSIRwOEx5eXly/uXLl7x69Yq1a9emnSUif06FSET+aYWFhdy9e5dEIoHX68XlchEKhcjLy2PGjPG/AgOBAOfPn087P5FIEAwGKS8vZ/369ZSWlnLq1Knk/MWLF/F6vRQVFaWdJSJ/Ti9mFBEZw9DQEMuWLaO9vZ2VK1dmJGN4eBin08mFCxfweDwZyRCRX6MVIhGRMeTk5HDu3LkJX+CYrlgsxuHDh1WGRP4CWiESERERy9MKkYiIiFieCpGIiIhYngqRiIiIWJ4KkYiIiFieCpGIiIhYngqRiIiIWJ4KkYiIiFieCpGIiIhYngqRiIiIWN5/2vR7aUt2V3kAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "By examples given above, users can easily understand the usage of inputs parameters. Similar to monitors, inputs can also be more complicate as a function form. BrainPy provides `fun_inputs` to receive the customized functional inputs created by users."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T04:05:07.830399Z",
     "start_time": "2025-10-06T04:05:06.214617Z"
    }
   },
   "source": [
    "def set_input():\n",
    "    net.E.input[:] = 20\n",
    "    net.I.input[:] = 20.\n",
    "\n",
    "\n",
    "runner8 = bp.DSRunner(target=net,\n",
    "                      monitors=['E.spike'],\n",
    "                      inputs=set_input,  # functional inputs\n",
    "                      jit=True)\n",
    "runner8.run(200.)\n",
    "bp.visualize.raster_plot(runner8.mon.ts, runner8.mon['E.spike'])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/2000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f61661c656cf4c7b9505b3fbd35073e7"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\adadu\\\\miniconda3\\\\envs\\\\bdp\\\\Lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 14
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
